latex‘h’ float specifier changed to ‘ht’

[1] Research Groups in Intelligent Machines (REGIM Lab), University of Sfax,
National Engineering School of Sfax (ENIS), BP 1173, 3038 Sfax,, Tunisia

# Exploring the Role of Convolutional Neural Networks (CNN) in Dental
Radiography Segmentation: A Comprehensive Systematic Literature Review

Walid Brahmi<EMAIL_ADDRESS>Imen Jdey
<EMAIL_ADDRESS>Fadoua Drira<EMAIL_ADDRESS>*

###### Abstract

In the field of dentistry, there is a growing demand for increased precision
in diagnostic tools, with a specific focus on advanced imaging techniques such
as computed tomography, cone beam computed tomography, magnetic resonance
imaging, ultrasound, and traditional intra-oral periapical X-rays. Deep
learning has emerged as a pivotal tool in this context, enabling the
implementation of automated segmentation techniques crucial for extracting
essential diagnostic data. This integration of cutting-edge technology
addresses the urgent need for effective management of dental conditions,
which, if left undetected, can have a significant impact on human health. The
impressive track record of deep learning across various domains, including
dentistry, underscores its potential to revolutionize early detection and
treatment of oral health issues.
Objective: Having demonstrated significant results in diagnosis and
prediction, deep convolutional neural networks (CNNs) represent an emerging
field of multidisciplinary research. The goals of this study were to provide a
concise overview of the state of the art, standardize the current debate, and
establish baselines for future research.
Method: In this study, a systematic literature review is employed as a
methodology to identify and select relevant studies that specifically
investigate the deep learning technique for dental imaging analysis. This
study elucidates the methodological approach, including the systematic
collection of data, statistical analysis, and subsequent dissemination of
outcomes.
Results: In incorporating 45 studies, we identified selection criteria and
research objectives, addressing significant gaps in the existing literature.
These studies assist clinicians in examining dental conditions and classifying
dental structures, including caries detection and the identification of
various tooth types. We evaluated model performance, addressing the identified
gaps, using diverse metrics that we strive to list and explain.

Conclusion: This work demonstrates how Convolutional Neural Networks (CNNs)
can be employed to analyze images, serving as effective tools for detecting
dental pathologies. Although this research acknowledged some limitations, CNNs
utilized for segmenting and categorizing teeth exhibited their highest level
of performance overall.

###### keywords:

Deep learning, Convolutional neural network, Dental imaging, Segmentation,
Evaluation Metrics

##  1 Introduction

Medical information plays a vital role in healthcare by aiding healthcare
professionals in making precise diagnoses, delivering effective treatments,
and informed decisions about patient care [1, 2]. Within the realm of medical
information, medical imaging stands out as an invaluable source.

In modern healthcare, medical imaging has become an essential tool, offering
crucial visual insights into the internal structures and conditions of the
human body [3]. Within dentistry, dental imaging refers to the use of various
imaging techniques to capture detailed images of the oral and maxillofacial
structures. These images are invaluable for diagnosing dental conditions,
planning treatments, and monitoring oral health. Dental imaging plays a vital
role in enabling dental professionals to visualize and evaluate the teeth,
jaws, and supporting structures.

Dental images can be acquired through different modalities, including X-rays,
computed tomography (CT), magnetic resonance imaging (MRI), and intraoral
cameras. Each modality offers specific advantages and is selected based on the
specific diagnostic requirements and clinical scenario.

Deep learning, in particular convolutional neural networks (CNNs) and computer
vision, has become a revolutionary area of dentistry. Thanks to the use of
these cutting-edge technologies, the dental industry is undergoing a period of
transformation in which the analysis of dental images and the integration of
artificial intelligence are revolutionizing many aspects of dental care [4].
By harnessing the capabilities of deep learning algorithms and computer vision
techniques, dentistry has the potential to dramatically improve diagnostic
performance, streamline treatment planning processes and, ultimately, improve
patient outcomes.

The utilization of CNNs in dentistry encompasses the detection of structures
like teeth and bone, as well as the identification of pathologies such as
caries and apical lesions. Additionally, CNNs are employed to segment images
by isolating the areas of interest and classify them based on specific
features like enamel caries lesions or cysts. However, it is important to
acknowledge the limitations within this field. One of the significant
weaknesses lies in the relatively small and private nature of the available
datasets, which restricts the diversity and size of the training data.
Moreover, some of the AI solutions developed in this domain may lack
robustness and stability, raising concerns about their reliability and
performance.

To analyze dental images for tasks like lesion detection, age or sex
determination, and human identification, tooth segmentation plays a vital
role. In the field of oral medicine, the automatic segmentation of teeth in
panoramic radiographs is a significant focus of research in image analysis.
However, segmenting teeth in panoramic radiographs presents challenges due to
the presence of other anatomical structures such as the chin, spine, and jaws.

Within the existing literature, a multitude of studies have employed
convolutional neural networks (CNNs) for tooth segmentation and identification
in dental images. These advanced deep learning techniques have exhibited
promising outcomes by leveraging the capabilities of CNNs to extract
meaningful features and accurately segment teeth in dental images.

This paper is organized as follows: Section 2 provides a summary of the
current state of the art in automatic tooth segmentation and references
relevant surveys in the field. In Section 3, we delve into the principles of
deep learning, exploring its various categories, common applications, and
specifically zooming in on the architecture of Convolutional Neural Networks
(CNNs) within the context of medical image analysis in computer vision. The
subsequent sections 4, 5, and 6 present the study methodologies, research
topics, and synthesis findings, offering detailed insights into the conducted
research. Finally, Sections 7 and 8 discuss the study’s limitations and
provide a conclusion.

##  2 Related work

In recent years, several surveys addressing deep learning-based dental image
segmentation have been conducted. This section provides a summary of related
studies, highlighting the key distinctions between this study and existing
surveys.

Schwendicke et al. (2019) [5] conducted a survey on the application of
Convolutional Neural Networks (CNNs) in dental and oral medicine imagery.
Their review encompassed 36 relevant articles and conference proceedings
spanning from 2015 to 2019, addressing clinical challenges in areas such as
general odontology, cariology, endodontics, periodontics, orthodontics, dental
radiology, forensic odontology, and general medicine.

Hwang et al. (2019) [6] published a survey on deep learning in oral and
maxillofacial radiology, identifying 25 pertinent papers up to December 2018
using PubMed, Scopus, and IEEE Explore databases. The study collected data on
deep learning architecture, training dataset size, evaluation results,
advantages and disadvantages, study objectives, and imaging modalities.

Kang et al. (2020) [7] reported on a prior study focusing on the application
of deep learning algorithms in dentistry and implantology. They analyzed 62
articles from MEDLINE and IEEE Xplore, categorizing them into tooth detection,
numbering, segmentation, and bone segmentation. The study included articles in
all languages and published before October 24, 2019, providing details on
Author Year, Architecture, Input, Output, and Performance metrics.

Prados-Privado et al. (2020) [8] conducted a systematic study visualizing the
state of artificial intelligence in dental applications, covering the
detection of teeth, caries, filled teeth, crowns, prostheses, dental implants,
and endodontic treatments. Utilizing three digital databases (PubMed, IEEE
Xplore, and arXiv.org), they identified 18 relevant papers. Notably, the study
did not include any segmentation methods based on deep learning.

Table 1 summarizes the central distinguishing factors between this review and
existing related work. Our contributions are outlined as follows:

  * •

We delve into a comprehensive exploration of Convolutional Neural Networks
(CNN), the preeminent deep learning algorithm. This involves an in-depth
explanation of concepts, theory, and contemporary architectures.

  * •

Our study thoroughly examines crucial challenges in Deep Learning, including
the shortage of training data, data imbalance, and sample data quality. We
also delve into proposed solutions to address these challenges.

  * •

We are trying to categorize a comprehensive list of medical imaging
applications using deep learning based on specific tasks.

Table 1: Comparison of this study with existing literature reviews on deep learning for dental image segmentation. Study |  Release year |  Total number of papers |  Limitations
---|---|---|---
[5] |  2019 |  36 |  Study includes studies that do not process dental images with deep learning
The elements impacting the model’s performance were not addressed
[6] |  2019 |  25 |  The elements impacting the model’s performance were not addressed
[7] |  2020 |  62 |  The study includes studies that do not deal with dental images.
The elements impacting the model’s performance were not addressed
[8] |  2020 |  18 |  The article does not deal with the segmentation task.
The elements impacting the model’s performance were not addressed

##  3 Background

###  3.1 Deep Learning

Deep learning (DL) holds significant potential to advance applications with a
real impact on the field of dentistry. Positioned within Machine Learning (ML)
as illustrated in Figure 1, DL empowers machines to emulate human intelligence
in increasingly sophisticated ways [9, 10]. To assess and distill knowledge
from extensive datasets, DL employs multiple layers of non-linear units. In a
basic scenario, there are two sets of neurons: one set receives input signals,
and the other set sends output signals. Upon receiving an input, the input
layer modifies it before passing it on to the next layer. While the layers in
a deep network may not be neurons, conceptualizing them as such proves
beneficial. This approach enables the algorithm to leverage multiple
processing layers comprising diverse linear and non-linear transformations
[11].

Figure 1: The Subsets of Artificial Intelligence.

Deep learning is part of the broader family of machine learning techniques
centered around learning data representations. An observation, such as an
image, can take various forms, including a set of edges, specific shape areas,
or, more abstractly, a vector of intensity values per pixel.

Several deep learning architectures, such as deep neural networks (DNN),
convolutional neural networks (CNN), recurrent neural networks (RNN), and
others, have found application in a wide array of fields. As depicted in
Figure 2, deep learning is frequently employed by partnerships to address
diverse and challenging issues falling into several categories, including:

  * •

Computer Vision: Deep learning is widely used in computer vision tasks such as
image classification, object detection, image segmentation, and facial
recognition [12].

  * •

Natural Language Processing (NLP): Deep learning models are used to process
and understand human language, including tasks such as language translation,
sentiment analysis, text generation, and question answering [13].

  * •

Speech Recognition: Deep learning techniques are employed in speech
recognition systems to convert spoken language into written text. This
technology is used in virtual assistants, transcription services, and voice-
controlled devices [14].

  * •

Healthcare: Deep learning is used in medical imaging analysis, disease
diagnosis, drug discovery, and personalized medicine. It helps in improving
accuracy, efficiency, and speed in various healthcare applications [15, 16].

  * •

Agriculture: Finding problematic environmental conditions [17].

  * •

Financial Services: Deep learning is employed in fraud detection, risk
assessment, algorithmic trading, credit scoring, and customer behavior
analysis in the financial industry [18].

  * •

Economics: Deep learning is employed in the field of economics to analyze and
comprehend intricate economic phenomena, predict economic behavior, analyze
economic data, and evaluate the effectiveness of policy interventions [19]
[20].

  * •

Robotics: Deep learning techniques are utilized in robotics for tasks such as
object recognition, grasping and manipulation, navigation, and human-robot
interaction [21].

  * •

Autonomous Driving :Significant improvements in the field of autonomous
driving have been made possible by the development of sensing, perception,
signal processing technologies and deep learning approaches. This has reduced
the effort required of human drivers and enhanced the safety of autonomous
driving [22].

  * •

Additional uses: These days, deep learning is applied in nearly every
industry. There are other further deep learning uses, including automated text
creation [23], game play [24], and picture captioning [25].

Figure 2: Common Deep Learning Applications.

####  3.1.1 Convolutional Neural Networks (CNNs)

A Convolutional Neural Network (CNN) is a specialized deep learning model
designed for processing data with a grid pattern, particularly images.
Comprising multiple layers, each with a distinct purpose, the CNN architecture
includes Convolutional layers, Rectified Linear Unit (ReLU) layers, pooling
layers, and a fully connected layer, as depicted in Figure 3. CNNs automate
feature extraction from images, eliminating the need for manual intervention.
The CNN architecture is bifurcated into two segments: convolution and densely
connected.

In convolutional layers, tensors known as feature maps are employed. A color
image, represented as a 3-dimensional tensor with three channels (red, green,
and blue - RGB), is denoted as (height, width, channels). To illustrate the
theoretical aspects of this network type, consider the example presented in
Figure 3, using a grayscale image of Width*Height pixels as input data. This
image corresponds to a shape tensor (Width, Height, 1), indicating the number
of neurons.

If Width=28 and Height=28, the tensor would consist of 784 neurons. For a
color image, the tensor shape would be (28, 28, 3), resulting in 2352 neurons.
It is important to note that the image, essentially a matrix where pixel
colors range from 0 to 255, is normalized to a color range between 0 and 1
before entering the network.

In a typical CNN architecture [26], multiple convolution layers and a pooling
layer are repeated several times, followed by one or more fully connected
layers. The process of transforming input data into output data through these
layers is known as forward propagation. The initial two layers, convolution,
and pooling are responsible for feature extraction. The convolution layer
applies filters to the input data, capturing distinct features or patterns.
The pooling layer then reduces the dimensionality of the feature maps,
retaining essential information while discarding some spatial details.

The final layer, the fully connected layer, maps the extracted features to the
ultimate output, such as classification. It connects every neuron in the
previous layer to the neurons in the current layer, allowing the network to
learn intricate relationships and make predictions based on the extracted
features.

Figure 3: Outline of CNN.

####  3.1.2 Categorization of Deep Learning methods

Similar to the field of machine learning, deep learning encompasses a diverse
range of methodologies aimed at deriving meaningful insights from data. As
figure 4 illustrates, deep learning techniques fall into four main categories
[27, 26]: supervised learning, unsupervised learning, semi-supervised learning
(also known as hybrid learning), and reinforcement learning.

Figure 4: Taxonomy of Deep Learning models.

##### Supervised Deep Learning

Supervised deep learning involves labeled data being used to train a model,
with each data point assigned a label or target value. The model learns from
labeled data by adjusting internal parameters to reduce the difference between
expected and actual labels. The ultimate aim of supervised learning is for the
model to generalize and predict the labels of previously unknown data points.
A second test data set is used to assess the performance of the trained model.
This test data set contains information that was not used during training but
has known true labels. Several supervised learning approaches are available
for DL, including recurrent neural networks (RNNs), convolutional neural
networks (CNNs), and deep neural networks (DNNs). In addition, the RNN
category covers techniques such as gated recurrent units (GRUs) and long
short-term memory (LSTM).

##### Unsupervised Deep Learning

Unsupervised learning emerges as a valuable technique when labeled data is
scarce or unavailable. In such scenarios, algorithms aim to identify inherent
patterns or relationships within the data without relying on predefined
labels. The primary focus is on acquiring essential features or
representations in the absence of explicit labels, contributing to the
exploration of underlying structures or relationships within the input data. A
variety of techniques are utilized in unsupervised learning, encompassing
generative networks like Generative Adversarial Networks (GANs),
dimensionality reduction methods such as auto-encoders (AE) and restricted
Boltzmann machines (RBM) known as Gibbs distribution, as well as clustering
algorithms [27]. These approaches play a crucial role in diverse tasks like
data clustering, dimensionality reduction, and anomaly detection. Despite the
advantages associated with unsupervised learning, particularly concerning
clustering – a method grouping similar data points based on specific criteria
– it has its limitations. These include the incapacity to provide precise
information about data categorization and the inherent computational
complexity.

##### Hybrid Deep Learning

In this method, the learning process relies on datasets that are only
partially labeled. Occasionally, both generative adversarial networks (GANs)
and DRL are applied similarly to this approach. One of the benefits of this
approach is its capacity to reduce the necessity for a large amount of labeled
data. On the flip side, a drawback of this technique is that irrelevant input
features in the training data may lead to inaccurate decisions.

##### Reinforcement Deep Learning

Reinforcement learning (RL) is the area focused on decision-making through the
acquisition of effective behavior in an environment with the goal of
maximizing rewards. This learned optimal behavior is a result of interactions
with the environment. Within RL, an agent possesses the ability to make
decisions, observe their consequences, and adjust strategies to develop an
optimal policy. In the initial stages, the agent observes the current state,
takes actions, and is rewarded along with the updated state. In this process
figure 5(a), the immediate reward and the new state hold the potential to
influence adjustments to the agent’s policy. This iterative sequence continues
until the agent’s policy gradually approaches proximity to the optimal policy.
DRL (Deep Reinforcement Learning) addresses the primary limitations of RL,
such as extended processing time required to achieve an optimal policy,
thereby introducing new possibilities within the DRL framework. Broadly
depicted in figure 5(b), DRL leverages the characteristics of deep neural
networks to enhance the learning process, resulting in improved speed and
algorithm performance. In DRL, deep neural networks maintain the internal
policy of the agent during interactions with the environment, determining the
next action based on the current state. DRL encompasses three main methods:
value-based, where the agent learns state or state-action values and acts
based on the best action in the given state; policy-based, which aims to
discover an optimal policy, whether stochastic or deterministic, for better
convergence in high-dimensional or continuous action spaces; and model-based,
which focuses on learning the functionality and dynamics of the environment
from previous observations, often using a specific model. In value-based and
policy-based methods, exploration of the environment is a crucial step. Model-
based DRL involves updating the model and replanning the process. Instances of
model-based DRL include imagination-augmented agents, model-based priors for
model-free approaches, and model-based value expansion. While these methods
are efficient when a model is available, challenges may arise with large state
spaces. In such cases, the model in model-based DRL is frequently updated, and
the process is replanned to achieve optimal outcomes [27].

(a) Reinforcement Learning Cycle

(b) Reinforcement Deep Learning Cycle

Figure 5: Comparison of Reinforcement Deep Learning Cycle and Reinforcement
Learning Cycle

###  3.2 Medical Images Analysis

In the field of medical image analysis, computer vision tasks such as image
classification, medical image segmentation, and object detection and
recognition are prevalent. Deep learning (DL) approaches, particularly those
based on convolutional neural networks (CNN), have shown superior performance
in handling these challenging problems across various medical applications. In
this section, we provide a comprehensive and technical evaluation of current
research that leverages the latest advancements in DL and CNN-based algorithms
to advance medical image analysis and understanding.

####  3.2.1 Object detection

Object detection has garnered significant interest among researchers in the
past few decades. With the remarkable progress in deep learning techniques,
the integration of artificial intelligence in healthcare, where object
detection plays a crucial role, has become more prominent.

Object detection refers to the identification and localization of instances of
specific objects within an image. This technique utilizes computer vision and
image processing to recognize and locate objects in still frames and videos.
Object detection systems accurately determine the number of objects present in
a given area, track their positions, and classify them.

The process of object detection involves bounding box annotation, where
objects are outlined with bounding boxes. The task revolves around recognizing
objects captured in an image, with each object category possessing distinct
characteristics for accurate categorization. Automated medical imaging can
effectively identify bone fractures, abnormal cellular activity, and other
medical conditions.

In the field of dentistry, several models have been developed to identify
various dental features, such as tooth decay detection [28, 29, 30, 31],
periapical lesion detection [32], and dental plaque detection [33]. Common
architectures utilized by researchers in the reviewed papers include Faster
R-CNN [34, 35], SSD [36], and YOLO [28, 37, 38]. Object detection involves two
main stages: feature extraction from the target and subsequent object
classification and localization.

####  3.2.2 Classification

Image classification is commonly employed to label an image or a series of
images as having specific diseases or not. Traditionally, image classification
involves extracting low or mid-level features to represent the image, followed
by employing a trainable classifier to determine the correct label. In recent
years, deep convolutional neural networks have demonstrated their superiority
over manually designed low-level and mid-level features in terms of high-level
feature representation. By combining feature extraction and classification
networks, deep convolutional neural networks provide a unified approach that
allows simultaneous training. For detailed information on DL-based medical
image classification methods in clinical applications, two excellent reviews
by Litjens and Ker [39, 40] are recommended. CNNs have also found applications
in dentistry for classifying medical images [41, 42, 43].

####  3.2.3 Image segmentation

Image segmentation is the process of dividing an image into meaningful sub-
regions or segments to identify regions of interest. It simplifies the
analysis and understanding of the entire scene in further image processing
stages. Segmentation involves partitioning a digital image into connected
pixels or regions that share common visual characteristics, such as intensity,
color, texture, histogram, or features [44]. As indicated in figure 6, there
are three primary segmentation strategies commonly used in building image
segmentation models: semantic segmentation 6(a), instance segmentation 6(b),
and panoptic segmentation 6(c). Semantic segmentation involves labeling each
pixel with a class label, indicating the category it belongs to. Instance
segmentation goes a step further by not only assigning class labels to pixels
but also distinguishing different instances of the same class. Panoptic
segmentation aims to combine the advantages of semantic and instance
segmentation to provide a comprehensive understanding of the image scene.

(a) Semantic segmentation

(b) Instance segmentation

(c) Panoptic segmentation

Figure 6: The three common type of image segmentation

These segmentation strategies play crucial roles in various medical image
analysis tasks, enabling precise identification and delineation of regions or
objects of interest, which can greatly aid in diagnosis, treatment planning,
and research in dentistry and other medical fields.

####  3.2.4 Others

In the realm of medical imaging, various tasks harness deep learning
techniques for enhanced outcomes. Image Reconstruction [45] utilizes these
methods to improve the quality of medical images, enhancing resolution,
reducing noise, and overall optimizing image quality. Concurrently, Image
Registration [46] involves aligning multiple medical images from different
modalities or time points, aiding in image comparison for treatment planning
and disease monitoring. Image De-noising [46] employs deep learning to
minimize noise and artifacts in medical images, ensuring clearer and more
accurate representations. Together, these applications showcase the
versatility of deep learning in advancing various aspects of medical image
analysis and interpretation.

##  4 Materials and methods

###  4.1 Method of Study

In this research, we employed the Systematic Literature Review (SLR) method.
SLR is a rigorous and structured approach that entails assessing,
interpreting, and identifying all existing research findings to address
specific research questions [47]. It adheres to a systematic process and
protocols to minimize bias and ensure an objective understanding [48]. The
objective of this SLR was to identify and analyze research trends,
methodologies, datasets, and frameworks pertaining to deep convolutional
neural network (CNN) approaches for dental segmentation.

###  4.2 Search and Selection Process

The literature review process involved several steps, as depicted in Figure 7,
outlining the study selection procedure for the SLR. Initially, all articles
relevant to deep CNN-based approaches for dental segmentation were identified
through a preliminary screening using specific search strings. Subsequently,
these articles were subjected to inclusion and exclusion criteria. Following
that, data extraction was conducted based on predefined criteria, and
pertinent information was extracted from the selected articles. Finally, the
collected data underwent analysis.

Out of a total of 642 articles initially identified, 45 articles were selected
for further study based on their relevance to the research domain. The
inclusion and exclusion criteria, along with the defined research protocol,
were applied consistently throughout the selection process to ensure the
quality and relevance of the selected articles.

By employing the SLR method, we aimed to provide a comprehensive and objective
analysis of the existing research on deep CNN-based approaches for dental
segmentation.

Figure 7: The systematic search and selection process.

####  4.2.1 Initial Search

Before commencing the search process, we conducted an initial exploration to
ensure the presence of a sufficient number of articles in the targeted field.
At this stage, we identified 45 articles discussing the enhancement of dental
imaging segmentation using deep Convolutional Neural Network (CNN) methods.
This confirmation validated the suitability of the topic for conducting a
Systematic Literature Review (SLR).

####  4.2.2 Manual Search

For the manual search, we selected appropriate search strings to capture a
broad spectrum of related articles. We utilized five online databases: IEEE
Xplore Digital Library [49], ACM Digital Library [50], Wiley online library
[51], ScienceDirect [52], and SpringerLink [53].

To mitigate false positive results, our search was confined to the titles,
abstracts, and keywords of the articles. When necessary, we adjusted the
search query to align with the requirements of each search engine. The search
command employed was: ((”dental radiography” OR ”dentistry”) AND (”Deep CNN
based”) AND (”segmentation”)). The manual search was conducted separately in
each database, and the results were amalgamated into a comprehensive
spreadsheet.

With the outlined search strategies, we identified a total of 642 relevant
papers. The distribution across databases is as follows: 81 from ACM Digital
Library, 33 from IEEE Xplore Digital Library, 269 from ScienceDirect Elsevier,
178 from SpringerLink, and 81 from Wiley Online Library. After eliminating
duplicate articles, 45 papers proceeded to the inclusion and exclusion process
(as detailed in Section 3.2).

Table 2: Total number of papers found in a database. |  Database Name |  Total number of papers found (N)
---|---|---
1 |  ACM Digital Library |  81
2 |  IEEE Xplore Digital Library |  33
3 |  Sciences Direct Elsevier |  269
4 |  Springer link |  178
5 |  Willey Online Library |  81
Total |  642
|  |

####  4.2.3 Selection of studies

The study selection for our systematic literature review involved the
application of predefined inclusion and exclusion criteria to the gathered
data. Inclusion criteria encompassed papers published within a 10-year
timeframe (2014-2023), written in English, and specifically addressing plant
disease detection using deep Convolutional Neural Networks (CNN). Papers
failing to meet these criteria, such as those published more than ten years
ago, written in languages other than English, or not primarily focused on
plant pathology detection with deep CNN, were excluded. The application of
these criteria facilitated the identification of a set of relevant studies for
subsequent analysis and synthesis.

Table 3: Inclusion and exclusion criteria.  Inclusion criteria |  Exclusion criteria
---|---
Ten years (2014 – 2023) |  More than ten years (2013 and less)
English language |  Other languages
Must include dental segmentation |  Not including dental segmentation
Must include Deep CNN |  Not including Deep CNN
Review articles |  Other types of scholarly publication

The selection process for relevant studies unfolded in three stages.
Initially, screening was conducted based on the titles of the papers, with
each title assessed for relevance to the study according to predefined
inclusion/exclusion criteria. Subsequently, screening extended to the
abstracts of the papers, where those meeting the inclusion criteria were
included, and those not meeting the criteria were excluded. Finally, the same
process was reiterated, this time utilizing the full text of the papers. In
instances where full texts were not accessible through online databases,
efforts were made to contact the authors or locate the texts from alternative
sources. If the full text could not be obtained, the paper was excluded from
the study.

##  5 Aims and Research Questions

The formulation of research questions can be organized based on four elements,
known as PICOC (Population, Intervention, Comparison, Outcomes, and Context).
Population refers to the targeted group of the research. Intervention is a
detailed aspect of the research or issues that attract researchers. Comparison
is defined as the aspect of the research where the intervention will be
compared. Outcomes are the results of the intervention, and Context is the
environment of the research [54]. Before starting the search, we determined
the research questions and formed the search string. According to Table 4,
seven research questions are proposed for this study.

Table 4: Research Questions. R_Q Id |  Research question |  Motivations
---|---|---
RQ1 |  What is the status of this field of study? |  Presents a chronological summary of the studies that were selected for this SLR and shows the trend of studies in this area in recent years.
RQ2 |  What was the key motivation for applying deep learning for dental image analysis? |  Analyze the different problems addressed by deep learning algorithms in the field of dental imaging.
RQ3 |  What deep learning implementation frameworks were used? |  Understanding the technical tools and frameworks utilized in a study or project is crucial, as it provides a comprehensive insight into the realm of deep learning.
RQ4 |  What deep learning algorithms were applied? |  To identify widely utilized deep learning models in dentistry, examining their applications, strengths, and potential contributions to diagnostics, treatment planning, and overall patient care. .
RQ5 |  What evaluation metrics are most frequently used to assess a deep Convolutional Neural Network segmentation model? |  Display the key ML or DL performance measures so that you may select the most appropriate ones for your use case.
RQ6 |  How can future studies build upon the findings of the reviewed papers to contribute novel insights or address existing limitations in the field? |  Assess the present state of the literature and recognize any deficiencies or constraints in the current research. Acknowledging these gaps creates a platform for upcoming studies to confront and surmount these challenges.
RQ7 |  What data sources were used? |  To train ML or DL models and to offer a baseline for evaluating the efficacy of the suggested architectures, numerous public datasets are presented.
|  |

##  6 Results

In this section, we expound upon the overall statistical findings derived from
the encompassed primary investigations and subsequently elucidate the outcomes
pertinent to the research inquiries within the subsequent sections. The roster
of the 45 primary studies incorporated into this Systematic Literature Review
is delineated in table LABEL:tab9.

###  6.1 The Current Landscape of Dental Radiography Segmentation

The 45 included studies are only in the form of journal articles. To associate
the articles with their country of origin, we analyzed the affiliations of the
authors of the included articles.

Figure 8: Countries of the included studies.

ranking highlights the prominence of Asian countries, with China leading with
10 articles, followed by Korea with 8 articles, and India with 4 articles.
Following closely are Turkey with 5 articles and the USA with 3 articles. The
list also features European countries, including Germany with 3 articles and
Spain with 2 articles. Additionally, other represented countries include Saudi
Arabia with 3 articles, and from the Asian continent, Malaysia, South Korea,
and Taiwan with 1 article each. From the African continent, South Africa and
Tunisia are represented with 1 article each.

Figure 9 provides an overview of the yearly growth rate of publications in
this field of study. An increase in the number of publications can indicate a
growing interest in the field, while a decrease could suggest that the field
is a closed research area. Notably, there is a limited representation of
papers from 2023, possibly due to restricted access to the most recent
publications at the time of the study, indicating that the actual number of
publications for that year may be higher. We observe an overall upward trend
in publications, implying that although the field was a relatively unpopular
research topic with no publications between 2014 and 2017, it has recently
attracted considerable attention from researchers.

Figure 9: Yearly papers on CNNs for dental X-ray segmentation.

###  6.2 Purposes of Deep Learning in Dental Image Segmentation

CNNs have emerged as powerful tools in image recognition, particularly in the
specialized domain of dental image segmentation. Leveraging CNN-based
approaches enables rapid and accurate recognition and classification of
various diagnostic processes, thereby advancing the field of dental image
processing. Radiographic 2D and 3D dental images play a pivotal role in
addressing a wide spectrum of dental issues.

In Table 5, we endeavor to outline the objectives and diseases addressed by
the 45 articles in our survey.

Basic Detection and Identification
---
Tooth detection and numbering | [35, 55, 56, 57]
Detection of Periapical Lesions and segmentation of cysts | [37, 58, 59, 32, 60]
Structural Analysis and Classification
Age and Sex determination | [61, 62, 63, 64]
Caries detection and segmentation | [28, 29, 30, 31, 36, 65, 66, 67]
Dental calculus identification | [33, 68]
Detection and classification of dental structures | [34, 42, 43, 69, 70, 71, 72, 73, 74, 75]
Advanced Condition Detection
Detecting dental conditions | [38, 41, 76, 77, 78, 79, 80]
Table 5: Automatic dental imaging segmentation purpose.

The tooth numbering process assigns a unique number to each tooth using the
universal tooth numbering system. In a dental panoramic image, encompassing
molars, premolars, canines, and incisors in both maxilla and mandible, the
maximum tooth count reaches 16 (Figure 6(a)). The distribution includes 6
molars, 4 premolars, 4 canines, and 8 incisors in each jaw.

In summary, recent advancements in deep learning, particularly convolutional
neural networks (CNNs), have significantly impacted dental image processing.
Researchers have successfully applied these techniques to tasks such as tooth
detection and numbering, periapical lesion detection, age and sex
determination, caries detection and segmentation, identification of dental
structures, and diagnosis of various dental conditions. The studies
highlighted demonstrate the effectiveness of CNN-based models, achieving high
precision and recall rates.

Notably, these applications extend to forensic medicine, aiding in age
estimation and gender identification based on dental characteristics. The
growing interest in this field is evident from the increasing number of
publications over the years.

Overall, the integration of deep learning approaches, as showcased in the
surveyed studies, holds promise for enhancing the accuracy and efficiency of
dental image analysis, contributing to improved diagnostic processes and
patient care.

###  6.3 Popular Deep Learning Frameworks

Continuous efforts from both the industry and the academic community have led
to the development of several widely adopted deep learning frameworks. Deep
learning frameworks serve as software libraries or platforms designed to
streamline the development, training, and deployment of deep neural networks.
A plethora of software frameworks are at our disposal for implementing Deep
Learning Models (DLM), and they undergo constant updates as fresh
methodologies and concepts surface. There exists a multitude of deep learning
frameworks, encompassing Theano, TensorFlow, Caffe, PyTorch, MXNet, MATLAB and
many others [81].

Indeed, it is crucial to emphasize that there is no universal solution that
can address every problem within the realm of machine learning. Quite often,
achieving success necessitates the utilization of a combination of diverse
tools and frameworks. Drawing from the results of our comprehensive literature
review, illustrated in Figure 10, it becomes evident that Keras, PyTorch, and
TensorFlow have surfaced as the dominant libraries in the realm of research.
This prominence can be attributed to the fact that both Keras and TensorFlow
platforms are open-source and readily accessible tools. Researchers tend to
favor these alternatives due to their lack of subscription fees, a significant
departure from MATLAB, where users often encounter expenses for access.

Figure 10: The distribution of papers per implementation framework.

In this section, we will examine various popular machine learning and deep
learning frameworks and libraries.

####  6.3.1 TensorFlow

TensorFlow is an open-source software library designed for numerical
computations using data flow graphs. It was created and is maintained by
Google’s Brain team in their Machine Intelligence research organization for
machine learning and deep learning applications. This library is tailored for
large-scale distributed training and inference tasks. In TensorFlow, nodes in
the computational graph represent mathematical operations, while the edges
represent the multidimensional data arrays known as tensors that flow between
these operations. The architecture of distributed TensorFlow includes master
and worker services with kernel implementations.

TensorFlow, as the most widely adopted deep learning framework [82], boasts
numerous unparalleled features. However, its development remains ongoing,
addressing various shortcomings. Here are some of TensorFlow’s notable
advantages:

  * •

Multi-language Support: TensorFlow offers support for multiple programming
languages, with the potential for future expansion to include even more
languages.

  * •

High Performance: TensorFlow delivers excellent performance and offers robust
support for various hardware configurations, including multi-CPU, GPU, or
hybrid platforms.

  * •

Real Portability: TensorFlow exhibits a high level of portability, surpassing
many other frameworks. Its close integration of research and product
development, coupled with tools like TensorFlow Serving, enables seamless
translation of innovative ideas into practical products.

####  6.3.2 Keras

Keras is an open-source neural network library renowned for its simplicity and
modularity. It is predominantly implemented in pure Python, making it
accessible to a wide range of users. Keras was specifically designed to
facilitate the process of experimenting with deep neural networks, and it
offers a comprehensive toolkit for building and training these networks. Some
key aspects of Keras include [83]:

  * •

Minimalist Framework: Keras is celebrated for its minimalist framework, which
reduces complexity and enables users to focus on the core aspects of deep
learning model development.

  * •

Modularity: Its modular design allows users to build neural networks by
assembling standalone, fully-configurable modules. This modularity extends to
layers, loss functions, activation functions, and more.

  * •

Ease of Experimentation: Keras simplifies the experimentation process, making
it easier to explore various deep learning architectures and ideas.

  * •

Compatibility: Keras exhibits compatibility with multiple backends or deep
learning frameworks, providing users with the flexibility to select the
backend that aligns best with their specific requirements and preferences.
This adaptability allows users to seamlessly integrate Keras into their
existing infrastructure and leverage the strengths of different frameworks for
their deep learning projects.

  * •

Versatility: Keras supports Convolutional Neural Networks (CNNs), Recurrent
Neural Networks (RNNs), and other deep learning models. It accommodates
multiple input and output structures, making it versatile for various tasks.

####  6.3.3 PyTorch

Facebook, Twitter, and Yandex have employed the Torch framework, available in
two iterations: the traditional version based on the LuaJIT language, and the
Python-based variant known as PyTorch, launched by Facebook in January 2017.
PyTorch [84], functioning as a Python package, offers two prominent features:
GPU-accelerated tensor calculations and the ability to construct dynamic
neural networks. These features distinguish PyTorch as a versatile and
powerful tool in the realm of deep learning.
In contrast to many other frameworks such as TensorFlow, which exhibit a
primarily static nature necessitating users to build and reuse fixed network
structures [82], PyTorch stands out with its dynamic approach. It leverages
Reverse-mode auto-differentiation, allowing users to modify network
performance with minimal delay or computational overhead. While this
technology is not exclusive to PyTorch, it boasts one of the most rapid and
efficient implementations, establishing PyTorch as a prominent player among
deep learning frameworks

####  6.3.4 Caffe

Caffe, developed by the University of California, Berkeley Vision and Learning
Center (BVLC), is a convolutional neural network framework designed around a
C++/CUDA/Python architecture. Jia Yangqing is credited as its creator [85].
Built upon feed-forward convolutional neural network architectures, such as
CNNs, Caffe offers multiple interfaces for command line, MATLAB, and Python.
Notably, it excels in feature extraction and seamlessly switches between CPU
and GPU, harnessing GPU acceleration for accelerated CNN learning. On April
18, 2017, Facebook introduced an enhanced iteration of Caffe known as Caffe2,
operating under the BSD license agreement. With its expanded feature set,
Caffe2 has the potential to surpass Caffe in popularity, emerging as a more
prominent deep learning framework in the future.

####  6.3.5 MXNet

In November 2016, Amazon introduced MXNet [86], an open source, lightweight,
portable, and flexible deep learning library developed by DMLC (Distributed
Machine Learning Community), which allows users to mix symbolic programming
mode and command programming mode to maximize efficiency and flexibility It
quickly gained prominence as one of the most performant frameworks. Following
its endorsement, Amazon expanded its services to include text-to-speech
recognition and an image analysis service called Recognition. MXNet is
officially recommended by Amazon Web Services (AWS) and is notably the first
framework to offer support for multi-GPU and distributed training,
distinguishing it from its peers. What sets MXNet apart is its wide range of
language bindings, making it accessible to developers using C++, Python, R,
Julia, MATLAB, and JavaScript. MXNet represents a powerful tool in the realm
of deep learning, offering efficiency, flexibility, and versatility to its
users.

It is also noteworthy that many research articles did not mention the specific
implementation platform used. It is essential to provide comprehensive details
regarding the implementation or to include a link to the code. Unfortunately,
some articles overlooked this aspect. We recommend that researchers not only
specify the deep learning implementation platform but also delve into other
technical aspects employed in their model development when writing their
papers for academic journals.

###  6.4 Applied Deep Learning Algorithms

In the background section, we have described Convolutional Neural Networks
(CNNs), which are highly relevant for deep learning. Below, we will discuss
the most commonly used types of deep learning networks that have been employed
in the reviewed studies, based on the publications we have selected.

Table 6: Summary of Pre-Trained Models Model |  Year |  Architecture |  Main contribution |  Limitations
---|---|---|---|---
LeNet [87] |  1998 |  Two convolutional (conv) layers for feature extraction. Two sub-sampling layers for spatial dimension reduction. Two fully connected layers for a global understanding of the input. An output layer featuring a Gaussian connection, suggesting the use of either a Gaussian activation function or Gaussian-weighted connections. |  The recognition of handwritten digits. |  • Inadequate adaptation to a variety of image classes.
• Utilization of oversized filters
• Limited capability in low-level feature extraction
VGG (Visual geometry group) [88] |  2014 |  Multiple layers of small 3x3 convolutional filters, followed by max Pool_ Layers. The fully connected layers use ReLU activation and dropout regularization to prevent overfitting. |  The increase in the depth of a network (number of layers) is a critical factor in improving performance. |  • Incorporation of computationally demanding fully connected layers.
GoogLeNet (Inception-V1) [89] |  2015 |  introduced the inception block in CNN. This block uses filters of different sizes to capture spatial information at various scales, aiming for high accuracy with reduced computational cost. It replaces traditional convolutional layers with these blocks, incorporating a 1x1 convolutional filter bottleneck for computation regulation. Sparse connections and global average pooling reduce redundancy, cutting parameters from 138 million to 4 million. |  The development of an inception module, which significantly reduces the network’s parameter count, leading to improved computational efficiency. |  • Cumbersome parameter customization owing to a heterogeneous topology
• Potential loss of valuable information due to a representational bottleneck
Inception V3 [90] |  2015 |  The architecture incorporates factorized 7x7 convolutions, spatial factorization using asymmetric convolutions, and an auxiliary classifier for label information propagation. Additionally, batch normalization and ReLU activation functions are applied. With around 23 million parameters |  Addresses the representational bottleneck by replacing large size filters with smaller ones. |  • Elaborate architecture design
• Absence of homogeneity
Faster R-CNN [91] |  2015 |  A single-stage model with end-to-end training, utilizing a novel Region Proposal Network (RPN) for efficient region proposal generation. It features key elements such as the fully convolutional RPN, which generates proposals at low computational cost, and the sharing of convolutional features between the RPN and the detection network. Training involves stochastic gradient descent (SGD) optimization for convolution layers, RPN weights, and the last fully connected layer weights. |  Region proposal network (RPN) |  • Complexity
• Training Time
• Detection Time
• Resource Intensiveness
• Model Complexity
U-Net [92] |  2015 |  A U-shaped encoder-decoder network architecture, consisting of four encoder blocks and four decoder blocks connected via a bridge. Each encoder block involves two 3x3 convolutions, followed by batch normalization and a ReLU activation function. The decoder network employs transposed convolutional layers for upsampling, aiming to restore the original image size. The inclusion of skip connections between encoder and decoder blocks facilitates the recovery of spatial information lost during downsampling. |  Skip connections. |  •Sensitivity to Input Size
• Hardware Memory Requirements
ResNet (Residual Network) [93] |  2015 |  ResNet utilizes residual blocks, which are stacked together to form the overall network. The original ResNet had 34 layers with 2-layer blocks, while advanced variants, like ResNet50, used 3-layer bottleneck blocks for improved accuracy and reduced training time. Skip connections in ResNet add outputs from previous layers to stacked layers, enabling the training of much deeper networks than was previously possible. |  Residual layer. |  • Complexity in architecture
• Degradation of feature-map information during feedforward
• Over-adaptation of hyperparameters for specific tasks due to the repeated
stacking of identical modules
• Numerous layers may contribute minimal or no information.
• Relearning of redundant feature maps may occur.
MobileNet [94] |  2017 |  The architecture comprises convolution layers, depthwise convolution layers with BN and ReLU, pointwise convolution layers with BN and ReLU, Global Average Pooling, Reshape, Dropout, additional Convolutional layers, Softmax, and Reshape. With approximately four million parameters, MobileNet is designed to be lightweight. It features two basic units: 3x3 Convolution and 3x3 Depthwise Convolution followed by 1x1 Convolution, using separable filters to reduce input channel numbers. |  MobileNet’s architecture is optimized for mobile and embedded devices, utilizing depthwise separable convolutions. Sequential layers include depthwise and pointwise convolutions with Batch Normalization and ReLU. |  • Challenges arise on resource-constrained micro-controllers.
• Sacrifices in accuracy compared to larger architectures, especially in
detailed tasks.
• Inherent tradeoff in accuracy due to its small size, though often minimal.
DenseNet [95] |  2017 |  DenseNet’s architecture utilizes Dense Blocks with densely connected layers to maximize feature reuse. Each block includes an input, output, and a growth rate (k) that regulates information flow to the next layer. Transition Layers connect dense blocks using 1x1 convolutions, 1x1 pooling, and 3x3 convolutions, striking a balance between model complexity and information flow. The Growth Rate (k) governs the number of additional features in each dense block, impacting information flow and network generalization. Finally, the Output Layer is typically a fully connected layer mapping input features to the desired class labels. |  Densely connected architecture |  • Memory Usage
YOLO (You Only Look Once) [96] |  2016 |  YOLO uses a fully convolutional neural network that passes the image through a series of convolutional layers to extract features. It predicts object boundaries and class labels simultaneously, making it a powerful and efficient deep learning model for object detection tasks. |  Real-time object detection |  • Detecting small or complex-shaped objects and accurately handling very large objects
Mask RCNN [97] |  2017 |  Incorporates a backbone network for feature extraction, a Region Proposal Network (RPN) for candidate region proposals, and a parallel branch for predicting segmentation masks alongside bounding box coordinates and class probabilities. This comprehensive framework excels in instance segmentation tasks, providing pixel-level masks for accurate object delineation. |  RoIAlign layer. |  • False alerts
• Missing labels

###  6.5 Evaluation Metrics for Deep CNN Segmentation in Dental Imaging

The tools or mechanisms for measuring model quality are known as evaluation
metrics, a crucial component in developing effective ML or DL models. These
metrics vary based on tasks, applications, and models. In the context of
dental image segmentation, we outline the evaluation metrics employed in the
selected papers.
Table LABEL:tab6 presents various performance measurement formulas. For a
comprehensive overview of commonly used evaluation measures, along with their
interpretation and implementation, refer to Table 7. These metrics are derived
from attributes in the Confusion Matrix (Figure LABEL:tab6), a two-dimensional
matrix providing information about Actual and Predicted classes. Key
attributes include:

| Predicted Class 0 | Predicted Class 1
---|---|---
Actual Class 0 | True Negative (TN) | False Positive (FP)
Actual Class 1 | False Negative (FN) | True Positive (TP)
Table 7: Confusion Matrix

  * •

TP True-Positive is the number of Positive cases classified correctly.

  * •

TN True-Negative is the number of Negative cases classified correctly.

  * •

FP False-Positive is the number of cases where the model predicted Positive
class, but the actual class was Negative. Also known as Type 1 Error.

  * •

FN False-Negative is the number of cases where the model predicted Negative
class, but the actual class was Positive. Also known as Type 2 Error.

Table 8: Performance Evaluation Metrics. Metric |  Formula |  Description |  Study
---|---|---|---

Accuracy (Pixel Accuracy (PA)) |
$\frac{NumberofCorrectPredictions(TP\,+\,TN)}{TotalNumberofPredictions(TP\,+\,TN\,+\,FP\,+\,FN)}$

$=\frac{(Number\,of\,correctly\,classified\,pixels)}{(Total\,number\,of\,pixels)}*100\%$ |  Is one way to determine how frequently the algorithm correctly classifies a data point. This is the number of items correctly identified as true positives or true negatives out of the total number of items. |  [28, 30, 31, 37, 38, 41, 42, 43, 55, 56, 58, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 72, 74, 75, 77, 79, 98, 99, 100]

Precision |
$\frac{TP}{TP\,+\,FP}$ |  Agreement between true class labels and machine predictions. It is calculated by adding all true positives and false positives in the system across all classes. |  [29, 34, 35, 38, 41, 42, 43, 61, 64, 65, 66, 67, 70, 75, 76, 78, 79, 57, 98, 99, 101]

Recall [(Sensitivity) (True Positive Rate TPR)] |
$\frac{TP}{TP\,+\,FN}$ |  The ability of a classifier to identify class labels. It is calculated by adding all true positives and false negatives in the system across all classes. |  [28, 29, 30, 31, 35, 37, 38, 41, 42, 43, 55, 56, 58, 59, 60, 61, 64, 65, 66, 67, 69, 70, 73, 75, 76, 78, 79, 98, 99]

Specifcity |
$\frac{TN}{FP\,+\,TN}$ |  This is referred to as the true negative rate. This function computes the proportion of actual negative cases predicted as negative by our model. |  [28, 30, 31, 34, 37, 41, 42, 55, 56, 58, 59, 60, 61, 64, 67, 69, 70, 73, 79, 99]

F1-score |
$2\,*\,\frac{\,Precision\,*\,Recall}{Precision\,+\,Recall}$ |  Measures the effectiveness of identification when recall and precision are of equal importance. |  [29, 30, 34, 37, 38, 41, 42, 43, 58, 59, 65, 75, 76, 79, 57, 98, 100]

Cohen’s Kappa |
$\frac{P_{0}\,-\,P_{e}}{1\,-\,P_{e}}$
where $P_{0}$ is the observed relative agreement between raters and $P_{e}$ is the hypothetical probability of chance agreement. |  Represents the level of precision and reliability in a statistical classification and assesses the degree of agreement between two raters (judges) who each assign items to mutually exclusive categories. |  [41, 62, 98, 100]
Dice similarity coefficient (DSC) |  $\frac{(2\,*\,area\,of\,overlap)}{(2\,*\,area\,of\,overlap)(Total\,number\,of\,pixels\,in\,bothimages),}$ |  Used to assess how well a predicted segmentation matches the corresponding ground truth in terms of pixel-level agreement. |  [32, 37, 56, 58, 64, 65, 69, 70, 77]
Receiver operating characteristic (ROC) |  Doesn’t have a specific formula |  ROC curve is graphical display of sensitivity (TPR) on y-axis and (1 – specificity) (FPR) on x-axis for varying cut-off points of test values. |  [31, 36, 73, 99]
Area under the curve (AUC) Average precision (AP)) |  AUC = $1-\frac{1}{2}(\frac{FP}{FP+TN}+\frac{FN}{FN+TP})$

AP =$\int_{r=0}^{1}p(r)\,\mathrm{d}r$

mean Average precision (mAP)
mAP =$\frac{1}{N}\sum_{k=1}^{K=n}AP_{k}$ |  As a single scalar value, AUC measures the performance of a binary classifier. It lies between [0.5–1.0], where the minimum value represents random classification and the maximum value represents perfect classification. |  [28, 31, 35, 43, 60, 63, 66, 68, 73, 78, 57, 99, 101]
Intersection over Union (IoU) [Jaccard index (JI)] |  $\frac{Area_{pred}\,\bigcap\,Area_{gt}}{Area_{pred}\,\bigcup\,Area_{gt}}$
$=\frac{TP}{TP+FP+FN}$ |  Used to evaluate the performance of object detection by comparing the ground truth bounding box to the preddicted bounding box. |  [33, 34, 35, 37, 56, 58, 74, 77, 78, 80]

Matthews correlation coefficient (MCC) |
$\frac{\text{TP}\cdot\text{TN}-\text{FP}\cdot\text{FN}}{\sqrt{(\text{TP}+\text{FP})\cdot(\text{TP}+\text{FN})\cdot(\text{TN}+\text{FP})\cdot(\text{TN}+\text{FN})}}$ |  The MCC is used for binary classification and is considered particularly useful in unbalanced class situations. takes values between 1 (perfect correlation between ground truth and predicted outcome) and -1 (inverse or negative correlation) - a value of 0 denotes a random prediction. |  [30] [37] [41]

In the field of evaluating dental image segmentation models, it is important
to note that, in addition to the mentioned metrics, some papers also utilize
specific measures such as Volumetric Overlap Error (VOE) and Relative Volume
Difference (RVD) [99]. These measures provide a thorough assessment of model
performance, especially when applied to volumetric tomographic images like
Cone Beam Computed Tomography (CBCT). Volumetric Overlap Error (VOE) measures
the degree of mismatch between segmented regions and actual regions, offering
insights into the accuracy of segmentation in terms of spatial localization.
Relative Volume Difference (RVD) quantifies volume differences between
segmented regions and true anatomical volumes, providing an assessment of
accuracy in terms of quantity. Thus, by considering these additional metrics,
researchers can obtain a more comprehensive evaluation of the performance of
dental image segmentation models, particularly when applied to volumetric
tomographic images such as CBCT scans.

As shown in Figure 11, and according to the papers studied we found that the 4
evaluation metrics used to assess the proposed models are Recall, Accuracy,
Precision and Specificity.

Figure 11: The summary of Performance evaluation measures reviewed in this
SLR.

Specificity and Sensitivity
Specificity and sensitivity are standardized and recognized measures for
evaluating performance in medical image segmentation. Sensitivity, also known
as recall or true-positive rate, focuses on the capabilities of detecting true
positives in pixel classification, while specificity, also known as the true-
negative rate, evaluates the capabilities of correctly identifying true-
negative classes (such as the background class). Specificity in image
segmentation tasks demonstrates the ability of the model to recognize the
background class in an image. Specificity ratios near 1 are expected due to
the large percentage of pixels labeled as background compared to the region of
interest (ROI). Therefore, specificity is a good statistic for checking the
model’s operation, but not so much for its performance.

Accuracy / Rand Index [102]
Accuracy, also known as the Rand Index or Pixel Accuracy, is defined as the
number of correct predictions, including positive and negative predictions,
divided by the total number of predictions. However, in the case of unbalanced
data, accuracy may not be the best measure of performance [41, 60]. Measuring
accuracy will always produce an unrealistic result due to the inclusion of
true negatives. Even when predicting the segmentation of an entire image as a
background class, accuracy rates are often greater than or close to 100%.
Therefore, the misleading accuracy measure is not suitable for evaluating
medical models based on the deep learning approach.

F-measure, IoU, and DSC
The F-measure, often known as the F-score, is one of the most extensively used
metrics for quantifying performance in computer vision. It is derived from the
sensitivity as well as the precision of a prediction, which analyzes the
overlap between the expected segmentation and the ground truth. However, by
considering accuracy, it also penalizes false positives, which are prevalent
in datasets with significant class imbalance. There are two prominent metrics
based on the F-measure: intersection over union (IoU), often known as the
Jaccard index or Jaccard similarity coefficient, and the Dice similarity
coefficient (DSC). DSC is referred to as the harmonic mean of sensitivity and
precision. The IoU penalizes under- and over-segmentation more than the DSC.
Even though these scores are suitable metrics and the most common metric in
validating medical image segmentation [69], they are not employed for
evaluating the performance of the suggested models in a majority of the
articles analyzed in our SLR.

ROC and AUC
The ROC curve, short for Receiver Operating Characteristic, is a line plot
that visualizes a classifier’s performance with different discriminating
thresholds. The performance is measured by comparing the true positive rate
(TPR) to the false positive rate (FPR). In the medical field, ROC curves are
widely accepted as a standard metric for comparing several classifiers when
assessing diagnostic tests. Hanley and McNeil [103] proposed the area under
the ROC curve (AUC) as a single-value performance assessment for diagnostic
radiology. The AUC measure is becoming a popular tool for validating machine
learning classifiers. It should be noted that an AUC has a value ranging from
0 to 1. A model with 100% incorrect predictions has an AUC of 0.0, while one
with 100% correct predictions has an AUC of 1.0.

Other metrics
For a segmentation system to be helpful and make a significant contribution to
the area, its performance must be systematically tested. Furthermore, the
evaluation must be carried out using conventional and well-known measures that
allow for fair comparison. Other metrics exist and can be used based on the
research topic and interpretive emphasis of the study. We refer to the
excellent studies of Taha et al. [104]. Additionally, Wang et al. [105]
describe the application of metrics in the evaluation of natural, medical, and
remote sensing images and present the supervised and unsupervised evaluation
methods in the application.

###  6.6 Public Datasets for Dental Radiography Analysis

Dental studies heavily rely on diverse datasets for optimal performance.
Addressing the last research question (RQ7), our investigation, as summarized
in Table LABEL:tab9, underscores that the majority of studies predominantly
utilized private datasets. Remarkably, only tree studies opted for public
datasets. Among the dataset types, panoramic radiographs emerged as the most
frequently employed, providing a comprehensive single-film view of the maxilla
and mandible, crucial for analyses such as orthodontic treatment and tooth
growth. Additionally, a subset of studies utilized Cone-beam computed
tomography (CBCT) for detailed three-dimensional (3D) imaging, while other
types, including periapical radiographs and bitewing radiographs, offered
vital diagnostic information for various dental conditions.

Our systematic literature review underscored a significant dearth of publicly
available reference datasets suitable for extracting pertinent features
related to oral medical conditions. Only three datasets were identified in the
public domain [106], [107], and [108]. Recognizing the pivotal role of data in
influencing the performance of machine learning (ML) and deep learning (DL)
models, the exploration of publicly available datasets, even in limited
quantity, offers immense potential to enhance the robustness and
generalizability of deep learning models in dentistry.

In the context of this scarcity, we would like to provide more detailed
insights into specific publicly available reference datasets, each
contributing uniquely to dental research:

  * •

The MLUA dataset, designed for dental caries segmentation in oral panoramic
images, provides full-size X-ray images, corresponding segmentation masks,
cropped images at tooth borders, and bounding box annotations. Accessible
athttps://github.com/Zzz512/MLUA, this resource supports research on accurate
lesion detection, and its code and test samples are available for
implementation.

  * •

Panetta et al [109], introduces The Tufts Dental Database comprises 1000
panoramic dental radiography images with expert annotations, facilitating
classification based on various criteria. Notably, it includes the
radiologist’s expertise through eye-tracking and think-aloud protocol
recordings. This dataset serves as a benchmark for state-of-the-art systems,
aiming to propel advancements in AI-driven abnormality detection, tooth
segmentation algorithms, and the integration of radiologist expertise into AI
applications.

  * •

Dental Radiography Dataset, packed in a zip file, includes 1269 .jpg images
along with three accompanying CSV files. Access the dataset at
https://www.kaggle.com/datasets/imtkaggleteam/dental-radiography/data.

  * •

the authors [110] collected dental panoramic radiographs from 106 pediatric
patients (2 to 13 years old). Using EISeg and LabelMe, they created the
world’s first dataset for caries segmentation and dental disease detection.
Additionally, 93 pediatric radiographs were combined with three adult datasets
(2,692 images) for a comprehensive segmentation dataset suitable for deep
learning

  * •

Lopez et al [111] introduces a database comprises 598 panoramic radiographs
with dimensions of 2041 x 1024, available in JPEG format. The annotations for
these panoramic radiographs are accessible at
]https://www.kaggle.com/datasets/humansintheloop/teeth-segmentation-on-dental-
x-ray-images

By highlighting these specific datasets, despite their limited number, we
emphasize their critical importance in developing more reliable and applicable
models. Access to these diverse datasets from different centers continues to
play an essential role in cross-center validation, ensuring the models’
performance in various clinical contexts. This approach, encouraging cross-
center collaboration and promoting generalization, remains essential to
advance the field of deep learning in dentistry.

###  6.7 How can future studies build upon the findings of the reviewed papers
to contribute novel insights or address existing limitations in the field?

This section explores the gaps revealed through the analysis of fundamental
studies in our systematic literature review, specifically focusing on the
segmentation of dental panoramic images. By highlighting the current deficits,
our objective is to establish a clear framework for future research in this
specific domain. This critical synthesis of gaps will serve as a robust
foundation for discussing potential directions in future research, paving the
way for innovative contributions to enhance and enrich dental panoramic image
segmentation. Following this, we will briefly present the main identified gaps
and envisioned avenues for future research. The prevalent gaps identified in
the literature review entail challenges linked to diverse facets. These
challenges are delineated in the subsequent table LABEL:tab8.

Table 9: Challenges, Gaps and Future Work  Aspects |  Limitations and studies |  Improving Solutions and Charting Future Paths
---|---|---
Dataset Size and source |  Dataset size is pivotal in deep learning model development as it directly impacts the model’s capacity to learn and generalize. A larger dataset provides more diverse examples, enabling the model to capture a broader range of patterns and variations present in real-world scenarios.
$-$ The dataset used was relatively small [28, 37, 42, 29, 33, 72].
$-$ To assess tooth-level or case-level Residual Bone Loss (RBL) for a large
cohort, the model requires more images for further optimization.[77]
$-$ The dataset is collected from a single organization or single device ,
limiting its diversity [36, 74, 69, 37, 38, 73, 59, 76, 29].
$-$ Absence of external test group [76, 30].
$-$ Heterogeneity in Image Sources [100] or Distribution of Heterogeneous
Images [41].
$-$ The data collected for the study were unbalanced due to high labeling costs. [80] |  $-$Employing data augmentation methods [28, 62].
$-$ Exploring Fine-tuning with transfer learning [34, 32].
$-$ Enriching the dataset with diverse data from multiple global sites and
from various devices [36, 74].
$-$ Exploring the integration of non-imagery data, such as patient history,
clinical signs, and symptoms, with imagery data within a deep learning system
[37, 41, 38].
$-$ Seek external validation of the neural network on diverse test sets to
assess generalizability.
Amasya et al. (2023)[100] showcased DiagnoCat AI’s adaptability across diverse
image sources and configurations, independent of specific devices or imaging
parameters https://diagnocat.com.
Image Quality |  The image quality defined by attributes such as clarity, sharpness, and minimal noise.
$-$ Poor-quality images, such as those with overlapping teeth or distorted
tooth length, can mislead the diagnosis, and the model does not automatically
exclude them [77, 42].
$-$ Capturing various aspects of a tooth and the difficulties associated with
internal tooth structure and the interproximal tooth surface in intraoral
photography [66, 56, 43, 100].
$-$ Handling Incorrectly Oriented Radiographs [56].
$-$ CBCT provides high resolution, but its accuracy in density measurement is inferior to traditional CT  |  $-$ Explore methods for preprocessing or filtering poor-quality images before analysis.
$-$ Improve the model’s robustness to handle variations in image quality.
$-$ Conduct an investigation into a more realistic simulated X-ray acquisition
process [75].
Data Processing and Labeling |  Refines raw data through operations like resizing and normalization, optimizing it for effective model training. Simultaneously, data labeling assigns categorical or numerical tags to each data point, providing crucial ground truth for supervised learning and enhancing the model’s ability to generalize and make accurate predictions.
$-$ Manual Mask Creation and Pre-processing poses a challenge due to its time-
consuming nature and the need for expertise, consequently restricting
scalability and potentially introducing errors [74].
$-$ In the benchmark dataset, certain categories are labeled more coarsely
than others [70].
$-$Complexities in Labeling: especially when premolar teeth are missing without clear spacing. [101] |  $-$ Investigate automated or semi-automated methods for mask generation. Explore unsupervised learning techniques for region identification [74].
$-$ The ambiguity in bounding box boundaries persists in object detection
[34].
Not a Replacement for Clinical Data |  While advantageous, deep learning models are not designed to replace the vital clinical data and expertise provided by healthcare professionals.
$-$ The resulting model has not been used in a clinical setting, and it is not
designed to replace periodontal charting and other clinical data.[77, 38, 63,
30].
$-$ The study acknowledges a low success rate in identifying dental caries and
calculus, making the AI models impractical for clinical use [76].
$-$
$-$ |  $-$ Explore seamless integration of the model’s predictions with periodontal charting and data.
Specific limitation |  Additional factors that impact the development, deployment, and effectiveness of deep learning models.
$-$ The model faces challenges in accurately identifying tooth numbers or
positions, particularly when multiple teeth are missing [77].
$-$ The model faces difficulties in precisely localizing specific dental
issues.[36, 74]
$-$ Increased Complexity and Training Time [80].
$-$ The study did not compare the diagnostic performance of CNNs with that of
dentists [28].
$-$ Image Dimensions and platform Memory Constraints (Google Colab) [41, 57].
$-$ Overfitting [62].
$-$ While capable of estimating age and sex with a single tooth, the models’
performance is anticipated to enhance with multiple teeth, potentially
limiting applicability in scenarios with few visible or accessible teeth [63].
$-$ Exemplary Use-Case Focus [59].
$-$ Determining the estimation success of each type of tooth (incisors,
canines, premolars, molars).
$-$ The Faster R-CNN network, while generally successful, encountered issues in recognizing two ’half teeth’ as a single intact tooth due to the inherent limitations of Convolutional Neural Networks (CNNs) not considering spatial relationships between image features [35]. |  Distinguishing Implants with Different Internal Connection Types [75] .
$-$ Understanding the trade-offs between image resolution and memory
constraints [41].
$-$ Dropout [62].
$-$ Investigating the impact of lower radiation doses on AI performance in
dental imaging.
$-$ Automated image analysis based on 3D imaging to assist clinicians in
lesion detection and contribute to the differential diagnosis of periapical
pathology and nonodontogenic lesions [32].

##  7 Limitations of the Study

In this systematic literature review (SLR), various deep learning-based
techniques for dental image segmentation were identified. However, there are a
few limitations of our SLR that should be taken into consideration. These
limitations are listed as follows:

  * •

Only journal articles discussing dental image segmentation were included in
this SLR. Through our search technique, several unrelated research
publications were discovered and excluded from this review during the
preliminary stages of the study. This ensured that the selected research
papers aligned with the investigation’s requirements. However, it is believed
that including additional sources such as books or conference papers could
have enhanced the review.

  * •

This review is limited to papers available in the English language.

  * •

It is possible that other digital libraries with relevant studies were
overlooked, although digital databases were considered when reviewing the
study articles.

  * •

The effect of hyperparameters such as the number of epochs, batch size, and
the number of layers on model performance has not been addressed in this
study.

##  8 Conclusion and future work

Over the past decades, there has been a notable increase in research interest
at the intersection of deep learning and oral healthcare analysis, leading to
continuous advancement in innovative models and methodologies year after year.
These algorithms have the capability to assist dentists in various aspects of
their clinical practice, including precise tooth identification, detection and
categorization of dental pathologies, assessment of prosthetic structures, and
error reduction. Deep learning algorithms hold considerable potential for
streamlining the clinical workflows of dentists, thereby resulting in improved
treatment outcomes.

The present study conducted a meticulous review of noteworthy articles
focusing on deep learning methodologies applied to dental image segmentation.
We systematically categorized these recent publications based on their
specific objectives and conducted an in-depth exploration of the predominant
evaluation metrics commonly used. Additionally, we addressed some of the
significant challenges that influence the performance of these models. We
anticipate that this study will provide readers with a comprehensive
understanding of the essential dimensions of this field, highlight the most
significant advancements, and shed light on potential avenues for future
research.

In our forthcoming research endeavors, we aspire to capitalize on the insights
gained from this rigorous systematic literature review (SLR). Our primary goal
is to design a novel algorithm for dental pathology segmentation by harnessing
the capabilities of deep learning. Importantly, we will also prioritize the
interpretability of deep learning methodologies to mitigate their inherent
opacity. Given the dynamic nature of this field, we envisage that this article
will take the form of an ongoing review, with regular updates to reflect the
emergence of new publications in this domain.

## Declarations

\- Conflicts of Interest
The authors declare that they have no known competing financial interests or
personal relationships that could have appeared to influence the work reported
in this paper.
\- Ethical Approval
Not applicable
\- Availability of supporting data
Data will be made available on request.
\- Competing interests
There are no conflicts of interest to report
\- Funding
Not applicable
\- Authors’ contributions

  * •

All authors conceptualized and wrote the main manuscript text.

  * •

Imen jdey conceptualized all tables and figures.

  * •

Walid Brahmi prepared all figures and tables .

  * •

Adel Alimi reviewed the final version of the manuscript.

  * •

All authors approved the final version of the manuscript.

\- Acknowledgments
This work was supported by the REGIM-Lab (Research Laboratory in Intelligent
Machines, code LR11ES48) at the National Engineering School of Sfax (ENIS),
University of Sfax, and the Faculty of Science and Techniques of Sidi Bouzid,
University of Kairouan, Tunisia.”

Table 10: list of reviewed studies. Authors |  Dataset characteristics |  Data Augmentation? |  Main objective& Architecture |  Task |  Performance
---|---|---|---|---|---
Brahmi et al[57], 2023 |  A public dataset consisting of 107 panoramic x-ray images[108]. |  No |  The Mask-RCNN network was utilized to perform pixel-level instance segmentation on a dataset1 containing 107 panoramic X-ray images of both young and adult patients. Subsequently, the Mask-RCNN network underwent training on dataset2, which comprises 73 panoramic X-ray images excluding those of young patients. |  Instance segmentation and Detection |  mAP = 90%, F1-sore = 63% and precision = 96%
Choi et al[98], 2023 |  Private dataset consisting of 402 periapical radiography (PA), 138 Dens evaginatus and 264 normal cases. Divided into training set (N = 282, 70%), validation set(N = 70, 18%) and test set (N = 50, 12%). |  Yes |  Five DL models in image classification, including a simple convolutional neural network (CNN) model, VGG, DenseNet, ResNet, abd InceptionResNetV2, were selected for the diagnosis of Dens evaginatus (DE). |  Classification |  F Model means (Full Image) and C Model mans (Cropped Image)
AUC:
F Model = 0.895, C Model = 0.901
Accuracy:
F Model = 0.828, C Model = 0.832
Precision:
F Model = 0.869, C Model = 0.856
Recall:
F Model = 0.871, C Model = 0.898
F1 Score:
F Model = 0.869, C Model = 0.876
Cohen’s Kappa:
F Model = 0.774, C Model = 0.684
Chun et al[99], 2023 |  A Private dataset included CBCT images from 50 patients, which were divided into a training set (64 volumes, 3546 images) and a test set (36 volumes, 1804 images). Each axial image, cropped to 512$*$512 pixels, served as input for the segmentation network. |  No |  Development and implementation of a distance-aware network for the automated classification of the three-dimensional (3D) positional relationship between an impacted mandibular third molar (M3) and the inferior alveolar canal (MC) in cone-beam CT (CBCT) images, incorporating the accurate segmentation capabilities provided by the Dense121 U-Net. |  Segmentation Classification |  Segmentation performance:
IoU = 0.872, DSC = 0.920, Precision = 0.946, Recall = 0.918
Classification performance:
Accuracy = 1.00, Sensitivity = 1.00, Specificity = 1.00, AUC = 1.00
Amasya et al[100], 2023 |  Private dataset consisting of 6000 panoramic radiographs with 45,161 annotated instances. |  No |  Two separate models are trained for different tasks. The first model uses Mask R-CNN with a pretrained ResNet-101 backbone to detect teeth, segment their masks, and define their numbering. The second model, based on the Cascade R-CNN architecture, is used for predicting periodontal bone loss. |  Segmentation
Classification |  For tooth conditions: Overall F-score, accuracy, and Cohen’s kappa coefficients were found to be 0.948, 0.977, and 0.933 for the binary results.
For multiclass results, the corresponding values were 0.992, 0.988, and 0.961.
For bone loss detection: Overall F-score, accuracy, and Cohen’s kappa
coefficients were found to be 0.985, 0.980, and 0.956 for the binary results.
For multiclass results, the corresponding values were 0.996, 0.993, and
0.974.%
Ali et al[101], 2023 |  Private dataset, initially comprising 3818 panoramic radiographs, was refined to 3138 images, with Experiment 1 utilizing 2615 for training and 523 for testing, while Experiment 2 employed a six-fold cross-validation on the entire set, essential for evaluating tooth and prosthesis detection models. |  Yes |  A novel method using two YOLOv7-based object detectors to enhance teeth detection in dental panoramic X-rays, addressing challenges posed by prosthetic treatments. The approach involves detecting tooth and prosthesis candidates, assigning approximate tooth numbers to prosthetic candidates, and optimizing the results, significantly improving detection efficiency. |  Detection
Classification |  Mean Average Precision (mAP) for tooth detection = 0.982.
Mean Average Precision (mAP) for prosthesis detection = 0.983.
Lee, C. et al[77], 2022 |  Private dataset. 693 periapical radiographic images , divided to 70%, 10%, and 20% for training, validation, and testing. |  No |  The task was to develop and evaluate different variations of the U-Net architecture for the segmentation of three different areas: the bone area, tooth, and cemento-enamel junction (CEJ) line. |  Segmentation |  bone area DSC = 0.96 , JI = 0.93 and PA = 0.96
tooth shape DSC = 0.95 , JI = 0.91 and PA = 0.89
CEJ line segmentations DSC = 0.91 , JI = 0.88 and PA = 0.99
Imak, A et al [67], 2022 |  Private dataset. 380 periapical images. |  No |  A score-based multi-input CNN ensemble (MI-DCNNE) for the automatic diagnosis of dental caries |  classification |  accuracy = 99.13, sensitivity = 98% , specificity = 100%, precision = 100% and F1-score = 98.99%
Zhang, X. et al [36], 2022 |  Private dataset. 3932 oral photographs. Divided into training set (N = 2507, 63.76%), validation set(N = 300, 7.63%) and test set (N = 1125, 28,6%). |  Yes |  Dental caries DL model adapted from Single Shot MultiBox Detector (SSD) |  Segmentation Classification |  ROC = 85.65%.
Oztekin, F. et al [74],2022 |  Private dataset. 250 panoramic images of 2048×1024px. Randomly divided into 70% for training, 20% for validation, and 10% for testing |  No |  A U-Net-based deep learning model for the automatic detection and classification of amalgam and composite fillings in panoramic images. |  Segmentation |  mean IoU = 0.767
Pixel Accuracy = 99.81%
Park, J. et al [80], 2022 |  Private dataset.1414 panoramic X-ray |  No |  a five-axis-based tooth recognition model using Faster R-CNN |  Segmentation |  IoU = 72%
Franco, A. et al [61], 2022 |  . 4003 panoramic radiographs. divided into the age groups “under 15 years” (n = 2,254) and “equal or older 15 years” (n = 1,749). |  Yes |  Eight CNN architectures was used InceptionV3, Xception, Inception ResNetV2, ResNet50, ResNet101, MobileNetV2, VGG16 and DenseNet121 DenseNet121 to distinguish females and males using dentomaxillofacial features from a radiographic image |  Segmentation Classification |  DenseNet121- from scratch
Accuracy = 71.64%, F1-score = 70.64 %, Precision = 71.59%, Recall = 69.8%,
Specificity = 87.47%
DenseNet121-transfer learning.
Accuracy = 82.18%, F1-score = 8.064% , Precision=0.8072% , Recall= 0.8056%,
Specifcity =0.9220%
Bayraktar, Y. et al [28], 2022 |  Private dataset. 1000 bitewing radiographic divided into training (80%) and testing-validation (20%) groups |  Yes |  Modified (YOLO) model to detect caries lesions |  Segmentation Classification |  accuracy = 94.59%, sensitivity = 72.26%, specificity = 98.19%and AUC = 87.19
Liu, M. et al [69], 2022 |  Private dataset. 254 CBCT. Divided into three subsets: the training set (154, 60.6%), the validation set (30, 11.8%), and the test set (70, 27.6%) |  Yes |  U-Nets used to mandibular third molar and mandibular canal segmentation
ResNet-34 used tomandibular third molar and mandibular canal relation classification: 3 classes |  Segmentation Classifcation |  segmentation of mandibular third molar: mean Dice similarity coefcient (mDSC) = 97% and a mean intersection over union (mIoU) = 96.06%;
segmentation of mandibular canal: mDSC = 92% and a mIoU of 90%.
classification: sensitivity = 90.2%,
specificity = 95.0%,
accuracy = 93.3%
Kohlakala, A. et al [75], 2022 |  Private dataset. 483 simulated X-ray images, which contain implants inserted into either human or pig jaws of size 512×512 and saved in JPEG format. |  Yes |  Two fully convolutional networks model FCN1 and FCN2 used to dental implant detection and recognition |  Segmentation Classification |  segmentation accuracy = 94.0%
classification accuracy = 71.7%
Moidu, N. et al [37], 2022 |  Private dataset. 3540 periapical root areas (PRA). Divided into two subsets: the training and validation set (3000 PRA) and the test set (540 PRA) |  Yes |  A CNN model based on YOLO.v3 architecture to score the periapical lesion of mandibular teeth |  Segmentation Classification |  Segmentation: Dice coefcient = 89% mean intersection over union (Jaccard index) = 90%, recall = 89%
precision = 90%
Classification: sensitivity = 92.1%
, specificity = 76%,
accuracy = 86.3%,
F1 score = 89%
Matthews correlation coefficient (MCC) = 71%.
Zhu, H. et al [65], 2022 |  Private dataset. 1159 panoramic images.Divided into three subsets: the training set (900), the validation set (135) and the test set (124). |  No |  CariesNet: an automated system for caries diagnosis that employs a U-shape encoder-decoder framework, a reverse attention mechanism, and a Res2Net backbone to achieve precise segmentation. |  Segmentation |  Dice coefficient = 93.64%,accuracy = 93.61%, precision = 94.09% and recall = 86.01%,
Park, E. et al [66], 2022 |  Private dataset. 2348 intraoral photographic randomly assigned to training (1638), validation (410), and test (300) datasets |  No |  Deep learning algorithm was utilized to perform tooth surface segmentation using U-Net, caries classification using ResNet-18, and lesion localization using Faster R-CNN. These techniques are applied in the context of caries detection.. |  segmentation
Classification |  segmentation accuracy = 81.39%, AUC = 83.7%,
classification accuracy =9 2.5%, sensitivity = 89.0% and precision = 87.4%
Alotaibi, G. et al [41], 2022 |  Private dataset. 1724 periapical images. Divided in a training dataset (n = 1206; 70%), a validation dataset (n = 345; 20%), and a test dataset (n = 173; 10%). |  No |  A CNN-based model VGG-16 to detect periodontal bone loss and classify the alveolar bone levels in teeth affected by periodontal disease. |  classification |  binary classification accuracy = 73.04% , precision = 70%, recall = 70% and Matthews correlation coefcient (MCC) = 51%
multi-classification: accuracy = 59.42%, precision = 83%, recall =70% and
Matthews correlation coefcient (MCC) = 0.65
Jang, W. et al [34], 2022 |  Data available on request from the authors. 300 periapical radiographic image. split into 80:20 ratio (train / test). |  No |  Evaluate the diagnostic performance of the Faster R-CNN model with ResNet 101 backbone for the tasks of object detection, classification, and localization. |  Detection
Classification
Localization |  classification performance:
precision = 97.7%, recall = 99.2% and F1 score = 98.4%
Segmentation performance
IoU=90.7%.
Mohammad, N. et al [62], 2022 |  Private dataset. 240 panoramic images. |  Yes |  Dynamic programming of active contour (DP-AC) and convolutions neural network model to segment the maturity development of the mandibular premolars. |  segmentation
Classification |  Classification : Cohen’s Kappa = 58%,
accuracy = 77%
Jiang, L. et al [38], 2022 |  Data available on request from the authors. 640 panoramic images. separated into a training set (80%) and a test set (20%) |  Yes |  UNet and YOLO-v4 were used to train a deep learning model for comprehensively diagnosing and staging periodontal alveolar bone loss. |  Segementation
Classification |  accuracy = 77%, Precision = 77%, Sensitivity= 77%, Specifcity = 88% and F1-score = 77%.
Kabir, T. et al [56], 2022 |  A public repository contained 116 panoramic images [107].
1240 intraoral radiographs obtained from a private database. |  No |  U-Net segmentation model with ResNet-34 as the backbone was utilized to accurately segment different structures and regions of interest in dental images, such as teeth, CEJ lines (Cementoenamel Junction), and the bone area. |  segmentation
Numbering |  Detection Precision=0.99
Detection Recall = 99%
Numbering Precision = 96%
Numbering Recall = 96%
Vila-Blanco, N. et al [63], 2022 |  Private dataset. 1746 dental panoramic radiographs (orthopantomograms or OPGs) was split into training, validation, and test subsets,which contained 60%, 20%, and 20% of the cases, respectively. |  Yes |  XAS: Automatic yet eXplainable Age and Sex determination. |  segmentation
Classification |  Tooth segmentation
<EMAIL_ADDRESS>= 96.4%<EMAIL_ADDRESS>=94.4%
Sex classification Accuracy = 91.8%
Bonfanti-Gris, M. et al [73], 2022 |  Private dataset. 300 panoramic radiographs exported in jpeg file |  No |  A pre-trained Convolutional Neural Network (CNN) called Denti.Ai to perform the tasks of detecting and classifying dental structures in panoramic radiographs. . |  segmentation
Classifcation |  Sensitivity = 56.92%
Specificity = 92.20%
Aljabri, M. et al [42], 2022 |  Private dataset. 416 panoramic radiographs |  Yes |  4 deep learning models were developed to classify the type of canine impaction (Type I or Type II) from panoramic dental radiographic images: DenseNet-121, VGG-16, Inception V3, and ResNet-50. |  Classification |  Experiment 1 Accuracy (unbalanced data : 282 samples represent Type I and 134 Type II )
Inception V3 0.8095
ResNet-50 0.7619
DenseNet-121 0.7976
VGG-16 0.6548
Experiment 2 Accuracy (balanced data : 134 samples represent Type I and 134
Type II )
Inception V3 0.9259
ResNet-50 87.04%
DenseNet-121 68.52%
VGG-16 57.41%.
Sivasundaram, S. et al [58], 2021 |  Data available on request from the authors. 1171 panoramic dental images, augmented into 3513. |  Yes |  Modified LeNet architecture for classifying the oral cyst images and a morphology-based segmentation method for segmenting the cyst regions in the classified cyst images |  segmentation
Classification |  sensitivity = 98.3%, specificity = 98.8%, an accuracy = 98.5%, precision =97.53%, F1 score = 98.06%, average DSC = 98.04 and IoU = 97.6.
Hsiao, T. et al [68], 2021 |  Private dataset. Optical coherence tomography images. 32000 B-scans for training, 8000 B-scans for validation data, and 8000 B-scans for testing |  No |  A VGG16-based automated model is employed to detect the presence of subgingival calculus and accurately identify the location of the lesion in optical coherence tomography (OCT) images. |  Detection
Localization |  accuracy = 95.06%.
AUC = 0.973.
Krois, J. et al [59], 2021 |  Two datasets each have 650 images available if needed within data protection regulation boundaries. |  Yes |  U-Net was used to evaluate the generalizability of deep learning models for the detection of apical lesions on panoramic radiographs and to identify methods for improving their performance. |  segmentation |  Model trained only on First dataset image :
F1-score = 54.1% ‘
Model trained only on second dataset image :
F1-score =32.7%
Cross-Dataset training
F1-score (First dataset) = 50.9%
F1-score (Second dataset) = 46.1%.
Basaran, M. et al [76], 2021 |  Private dataset. optical coherence tomography images. 1084 dental panoramic radiographs. |  No |  AI model based on Faster R-CNN and Google Net Inception v2 to detect dental conditions. |  segmentation |  best sensitivity = 96.74%, best precision= 92.59% and the F1-score = 94.33%.
Chen, H. et al [78], 2021 |  Private dataset. optical coherence tomography images. 2900 digital dental periapical radiographs |  No |  Faster R-CNNs to detect diseases including decay, periapical periodontitis, and periodontitis in dental periapical radiographs. |  Detection
localization |  decay
IoU = 0.71, Precision = 0.61, Recall = 0.54 and AP = 0.45
periapi
IoU =0.69, Precision = 0.51, Recall = 0.51 and AP = 0.36
periodo
IoU = 0.68, Precision = 0.56, Recall = 0.61 and AP =0.43.
Lee, S. et al [29], 2021 |  Private dataset. 304 bitewing radiographs. Divided into 149 radiographs for training on both structure and caries segmentation, 105 radiographs for training on caries segmentation, and 50 radiographs with no dental caries. |  Yes |  The U-Net architecture is used for the early detection of initial dental caries (tooth decay). |  Detection |  precision = 63.29%;
recall = 65.02%;
F1-score = 64.14%.
Bilgir, E. et al, 2021 |  Data available on request from the authors. 2482 panoramic radiographs. Divided into training (80%), validation (10%), and test (10%) groups. |  No |  Faster R-CNN Inception v2 model, was used to automatically detect and number teeth on the panoramic radiographs. |  segmentation |  Sensitivity (Recall) = 0.9559
Precision = 0.9652
F1Score = 0.9606.
Chen, Q. et al [43], 2021 |  Private dataset. 175 full-jaw 3D tooth models |  Yes |  A basic CNN model for 8-class tooth type classification (first premolar, second premolar, first molar, second molar in maxilla and mandible respectively) |  classification |  accuracy = 91.35%,
precision = 91.49%,
recall = 91.29%,
F1-score = 0.91
Rajee, M. et al [64], 2021 |  Private dataset. 1000 panoramic dental images divided into 600 training samples and 400 testing samples |  No |  Gender classification on digital dental x-ray images using deep convolutional neural network model based on Resnet50 architecture. |  Segmentation
Classification |  dice = 0.94
accuracy = 98.27
Sensitivity = 98.04
Specificity = 98.51
Precision = 98.52
Singh, P. et al [55], 2020 |  Private dataset. 400 panoramic dental images, divided into 240 training samples and 160 testing samples |  Yes |  Numbering and Classification of Panoramic Dental Images Using 6-Layer Convolutional Neural Network (DCNN) |  Pre-processing
Segmentation
Numbering
classification (4 classes molar, premolar, canine and incisor). |  Augmented database
accuracy = 95%
Sensitivity = 98%
Specificity = 92%

Original dataset
accuracy = 92%
Sensitivity = 90%
Specificity = 85%
You, W. et al [33], 2020 |  Data available on request from the authors. 984 intraoral divided into 886 training samples and 98 testing samples. |  No |  Deep learning-based dental plaque detection on primary teeth based on DeepLab and DeepLabV3 architectures . |  segmentation |  mean intersection-over-union (MIoU) = 0.726
Kwak, G. et al [71], 2020 |  Data available on request from the authors. 49094 CBCT images, divided into train:valid:test sets with the ratio of 6:2:2. |  No |  An Automatic mandibular canal detection based on 2D SegNet, 2D and 3D U-Nets architectures. |  Segmentation |  2D U-Net accuracy = 82%
2D SegNet accuracy = 96%
3D U-Net accuracy = 99%.
Zhao, Y. et al [70], 2020 |  Abenchmark public dataset. 1500 panoramic X-ray images divided into 1200 training samples, 150 validation samples and 150 testing samples. |  No |  A Two-Stage Attention Segmentation Network (TSASNet) on dental panoramic X-ray images |  segmentation |  Accuracy = 96.94
Specificity = 97.81
Precision = 94.97
Recall = 93.77
Dice = 92.72
Cantu, A. et al [30], 2020 |  Private dataset. 3686 bitewing radiographs. The data was divided into a training (3293 images ), validation (252 images ) and test dataset (141 images) |  No |  A deep learning model based on U-net to detect caries lesions on bitewing radiographs |  Segmentation |  Accuracy 0.80
Sensitivity 0.75
Specificity 0.83
F1score 0.73
MCC 0.57
Setzer, F. et al [32], 2020 |  Private dataset. 20 CBCT images were divided into 16 CBCT images) were used for training, and 4 CBCT images was used for validation |  Yes |  A Deep Learning model for the automated segmentation of cone-beam computed tomographic (CBCT) images and the detection of periapical lesions. Based on a U-Net architecture. |  Segmentation |  DICE index
Lesion 0.52
Tooth structure 0.74
Bone 0.78
Restorative materials 0.58
Background 0.95
Chen, H. et al [35], 2019 |  Data available on request from the authors. 1250 periapical film divided into training (800), validation (20.), and test (250) groups |  No |  A deep learning approach for automatic teeth detection and numbering based on faster R-CNN (A total of 32 teeth classes were required to be recognized in the X-ray images) |  segmentation |  Numbering
precision = 0.98
recall = 0.98
Mean IOU = 0.91

Numbering
precision = 0.91
recall = 0.91
Al Kheraif, A. et al [79], 2019 |  Private dataset. 1500 panoramic images divided into training of 800 with testing of 7000 |  No |  Histogram enhancement with a convolutional neural network (CNN) to detect dental diseases |  segmentation
classification |  Segmentation : Accuracy = 0.912; Specificity = 0.967; Precision = 0.89; Recall = 0.92 and F1 score = 0.94
classification Accuracy = 97.07%
Ekert, T. et al [60], 2019 |  Private dataset. A synthesized data set of 2001 tooth segments from panoramic radiographs |  Yes |  A deep convolutional neural networks (CNNs) to detect apical lesions (ALs). |  segmentation |  AUC = 0.85, sensitivity = 0.65 and specificity = 0.87
Tian, S. et al [72], 2019 |  Private dataset. 600 dental models |  No |  An automatic segmentation and classification method for 3D dental model via 3D CNN. |  Segmentation
classification : seven tooth types which are the central incisors, lateral incisors, canines, first premolars, second premolars, first molars and second molars. |  classification accuracy in Level1 network is 95.96%, the average classification accuracy in Level2 network is 88.06%, and the accuracy of tooth segmentation is 89.81%
Lee, J. et al [31], 2018 |  Private dataset. 3000 periapical radiographic, divided into a training and validation dataset (n = 2400 [80%]) and a test dataset (n = 600 [20%]) |  Yes |  GoogLeNet Inception v3 for detection and diagnosis of dental caries on periapical radiographs. |  Segmentation
classification |  premolar accuracy = 89.0% , molar accuracy = 88.0% , both premolar and molar accuracy = 82.0%
AUC = 0.917 on premolar, AUC = 0.890 on molar, and AUC = 0.845 on both
premolar and molar models.

## References

  *   * Mogli [2009] Mogli, G.: Medical records role in healthcare delivery in 21st century. Acta Informatica Medica 17(4), 209–212 (2009)
  * Pickering et al. [2013] Pickering, B.W., Gajic, O., Ahmed, A., Herasevich, V., Keegan, M.T.: Data utilization for medical decision making at the time of patient admission to icu. Critical care medicine 41(6), 1502–1510 (2013)
  * Abedalla et al. [2021] Abedalla, A., Abdullah, M., Al-Ayyoub, M., Benkhelifa, E.: Chest x-ray pneumothorax segmentation using u-net with efficientnet and resnet architectures. PeerJ Computer Science 7, 607 (2021)
  * Khanna and Dhaimade [2017] Khanna, S.S., Dhaimade, P.A.: Artificial intelligence: transforming dentistry today. Indian J Basic Appl Med Res 6(3), 161–167 (2017)
  * Schwendicke et al. [2019] Schwendicke, F., Golla, T., Dreher, M., Krois, J.: Convolutional neural networks for dental image diagnostics: A scoping review. Journal of dentistry 91, 103226 (2019)
  * Hwang et al. [2019] Hwang, J.-J., Jung, Y.-H., Cho, B.-H., Heo, M.-S.: An overview of deep learning in the field of dentistry. Imaging science in dentistry 49(1), 1–7 (2019)
  * Kang et al. [2020] Kang, D.-Y., Duong, H.P., Park, J.-C.: Application of deep learning in dentistry and implantology. Journal of implantology and applied sciences 24(3), 148–181 (2020)
  * Prados-Privado et al. [2020] Prados-Privado, M., Villalón, J.G., Martínez-Martínez, C.H., Ivorra, C.: Dental images recognition technology and applications: a literature review. Applied Sciences 10(8), 2856 (2020)
  * Hcini et al. [2021] Hcini, G., Jdey, I., Heni, A., Ltifi, H.: Hyperparameter optimization in customized convolutional neural network for blood cells classification. J. Theor. Appl. Inf. Technol 99, 5425–5435 (2021)
  * Hinton [2018] Hinton, G.: Deep learning—a technology with the potential to transform health care. Jama 320(11), 1101–1102 (2018)
  * LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. nature 521(7553), 436–444 (2015)
  * Szeliski [2022] Szeliski, R.: Computer Vision: Algorithms and Applications. Springer, ??? (2022)
  * Bharadiya [2023] Bharadiya, J.: A comprehensive survey of deep learning techniques natural language processing. European Journal of Technology 7(1), 58–66 (2023)
  * Roger et al. [2022] Roger, V., Farinas, J., Pinquier, J.: Deep neural networks for automatic speech processing: a survey from large corpora to limited data. EURASIP Journal on Audio, Speech, and Music Processing 2022(1), 19 (2022)
  * Yu et al. [2023] Yu, Z., Wang, K., Wan, Z., Xie, S., Lv, Z.: Popular deep learning algorithms for disease prediction: a review. Cluster Computing 26(2), 1231–1251 (2023)
  * Egger et al. [2022] Egger, J., Gsaxner, C., Pepe, A., Pomykala, K.L., Jonske, F., Kurz, M., Li, J., Kleesiek, J.: Medical deep learning—a systematic meta-review. Computer methods and programs in biomedicine 221, 106874 (2022)
  * Ahmad et al. [2023] Ahmad, A., Saraswat, D., El Gamal, A.: A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools. Smart Agricultural Technology 3, 100083 (2023)
  * Huang et al. [2020] Huang, J., Chai, J., Cho, S.: Deep learning in finance and banking: A literature review and classification. Frontiers of Business Research in China 14(1), 1–24 (2020)
  * Zheng et al. [2023] Zheng, Y., Xu, Z., Xiao, A.: Deep learning in economics: a systematic and critical review. Artificial Intelligence Review, 1–43 (2023)
  * [20] Bouzidi, S., Jdey, I., Alimi, A.: A vision transformer approach with l2 regularization for sustainable fashion classification. Available at SSRN 4686032
  * Soori et al. [2023] Soori, M., Arezoo, B., Dastres, R.: Artificial intelligence, machine learning and deep learning in advanced robotics, a review. Cognitive Robotics (2023)
  * Muhammad et al. [2020] Muhammad, K., Ullah, A., Lloret, J., Del Ser, J., Albuquerque, V.H.C.: Deep learning for safe autonomous driving: Current challenges and future directions. IEEE Transactions on Intelligent Transportation Systems 22(7), 4316–4336 (2020)
  * Narayan and Gardent [2022] Narayan, S., Gardent, C.: Deep Learning Approaches to Text Production. Springer, ??? (2022)
  * Hazra and Anjaria [2022] Hazra, T., Anjaria, K.: Applications of game theory in deep learning: a survey. Multimedia Tools and Applications 81(6), 8963–8994 (2022)
  * Xiao et al. [2022] Xiao, F., Xue, W., Shen, Y., Gao, X.: A new attention-based lstm for image captioning. Neural Processing Letters 54(4), 3157–3171 (2022)
  * Alzubaidi et al. [2021] Alzubaidi, L., Zhang, J., Humaidi, A.J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M.A., Al-Amidie, M., Farhan, L.: Review of deep learning: Concepts, cnn architectures, challenges, applications, future directions. Journal of big Data 8, 1–74 (2021)
  * Talaei Khoei et al. [2023] Talaei Khoei, T., Ould Slimane, H., Kaabouch, N.: Deep learning: Systematic review, models, challenges, and research directions. Neural Computing and Applications 35(31), 23103–23124 (2023)
  * Bayraktar and Ayan [2022] Bayraktar, Y., Ayan, E.: Diagnosis of interproximal caries lesions with deep convolutional neural network in digital bitewing radiographs. Clinical oral investigations 26(1), 623–632 (2022)
  * Lee et al. [2021] Lee, S., Oh, S.-I., Jo, J., Kang, S., Shin, Y., Park, J.-w.: Deep learning for early dental caries detection in bitewing radiographs. Scientific Reports 11 (2021)
  * Cantu et al. [2020] Cantu, A.G., Gehrung, S., Krois, J., Chaurasia, A., Rossi, J.G., Gaudin, R.A., Elhennawy, K., Schwendicke, F.: Detecting caries lesions of different radiographic extension on bitewings using deep learning. Journal of dentistry, 103425 (2020)
  * Lee et al. [2018] Lee, J.-H., Kim, D.-H., Jeong, S.-N., Choi, S.-H.: Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. Journal of dentistry 77, 106–111 (2018)
  * Setzer et al. [2020] Setzer, F.C., Shi, K.J., Zhang, Z., Yan, H., Yoon, H., Mupparapu, M., Li, J.: Artificial intelligence for the computer-aided detection of periapical lesions in cone-beam computed tomographic images. Journal of endodontics (2020)
  * You et al. [2020] You, W., Hao, A., Li, S., Wang, Y., Xia, B.: Deep learning-based dental plaque detection on primary teeth: a comparison with clinical assessments. BMC Oral Health 20 (2020)
  * Jang et al. [2022] Jang, W.S., Kim, S., Yun, P.S., Jang, H.S., Seong, Y.W., Yang, H.S., Chang, J.-S.: Accurate detection for dental implant and peri-implant tissue by transfer learning of faster r-cnn: a diagnostic accuracy study. BMC Oral Health 22(1), 1–7 (2022)
  * Chen et al. [2019] Chen, H., Zhang, K., Lyu, P., Li, H., Zhang, L., Wu, J., Lee, C.-H.: A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films. Scientific Reports 9 (2019)
  * Zhang et al. [2022] Zhang, X., Liang, Y., Li, W., Liu, C., Gu, D., Sun, W., Miao, L.: Development and evaluation of deep learning for screening dental caries from oral photographs. Oral diseases 28(1), 173–181 (2022)
  * Moidu et al. [2022] Moidu, N.P., Sharma, S., Chawla, A., Kumar, V., Logani, A.: Deep learning for categorization of endodontic lesion based on radiographic periapical index scoring system. Clinical Oral Investigations 26(1), 651–658 (2022)
  * Jiang et al. [2022] Jiang, L., Chen, D., Cao, Z., Wu, F., Zhu, H., Zhu, F.: A two-stage deep learning architecture for radiographic staging of periodontal bone loss. BMC Oral Health 22(1), 106 (2022)
  * Singha et al. [2021] Singha, A., Thakur, R.S., Patel, T.: Deep learning applications in medical image analysis. Biomedical Data Mining for Information Retrieval (2021)
  * Litjens et al. [2017] Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., Van Der Laak, J.A., Van Ginneken, B., Sánchez, C.I.: A survey on deep learning in medical image analysis. Medical image analysis 42, 60–88 (2017)
  * Alotaibi et al. [2022] Alotaibi, G., Awawdeh, M., Farook, F.F., Aljohani, M., Aldhafiri, R.M., Aldhoayan, M.: Artificial intelligence (ai) diagnostic tools: utilizing a convolutional neural network (cnn) to assess periodontal bone level radiographically—a retrospective study. BMC Oral Health 22(1), 399 (2022)
  * Aljabri et al. [2022] Aljabri, M., Aljameel, S.S., Min-Allah, N., Alhuthayfi, J., Alghamdi, L., Alduhailan, N., Alfehaid, R., Alqarawi, R., Alhareky, M., Shahin, S.Y., et al.: Canine impaction classification from panoramic dental radiographic images using deep learning models. Informatics in Medicine Unlocked 30, 100918 (2022)
  * Chen et al. [2021] Chen, Q., Huang, J., Salehi, H.S., Zhu, H., Lian, L., Lai, X., Wei, K.: Hierarchical cnn-based occlusal surface morphology analysis for classifying posterior tooth type using augmented images from 3d dental surface models. Computer methods and programs in biomedicine 208, 106295 (2021)
  * Zhu et al. [2021] Zhu, Z., Li, D., Hu, Y., Li, J., Liu, D., Li, J.: Indoor scene segmentation algorithm based on full convolutional neural network. Neural Computing and Applications 33, 8261–8273 (2021)
  * Pauwels [2021] Pauwels, R.: A brief introduction to concepts and applications of artificial intelligence in dental imaging. Oral radiology 37(1), 153–160 (2021)
  * Kaji and Kida [2020] Kaji, S., Kida, S.: Overview of image-to-image translation by use of deep neural networks: denoising, super-resolution, modality conversion, and reconstruction in medical imaging. Igaku Butsuri: Nihon Igaku Butsuri Gakkai Kikanshi= Japanese Journal of Medical Physics: an Official Journal of Japan Society of Medical Physics 40(4), 139–139 (2020)
  * Keele et al. [2007] Keele, S., et al.: Guidelines for performing systematic literature reviews in software engineering. Technical report, ver. 2.3 ebse technical report. ebse (2007)
  * Wahono [2020] Wahono, R.S.: Systematic literature review: Pengantar, tahapan dan studi kasus. Romisatriawahono. Net (2020)
  * [49] IEEE: Institute of Electrical and Electronics Engineers. [Online; accessed 13-January-2023]. https://ieeexplore.ieee.org/Xplore/home.jsp
  * [50] ACM: a research, discovery and networking platform. [Online; accessed 13-January-2023]. https://dl.acm.org/
  * [51] Library, W.O.: a digital library of scholarly resources, journals, books, and references. [Online; accessed 13-January-2023]. https://onlinelibrary.wiley.com/
  * [52] ScienceDirect: Elsevier’s premier platform of peer-reviewed scholarly literature. [Online; accessed 13-January-2023]. https://www.sciencedirect.com/
  * [53] SpringerLink: a reading platform of choice for hundreds of thousands of researchers worldwide. [Online; accessed 13-January-2023]. https://link.springer.com/
  * James et al. [2021] James, M.-S., Marrissa, C., Mark, S., Anthea, B., et al.: Systematic approaches to a successful literature review. Systematic Approaches to a Successful Literature Review, 1–100 (2021)
  * Singh and Sehgal [2020] Singh, P., Sehgal, P.: Numbering and classification of panoramic dental images using 6-layer convolutional neural network. Pattern Recognition and Image Analysis 30, 125–133 (2020)
  * Kabir et al. [2022] Kabir, T., Lee, C.-T., Chen, L., Jiang, X., Shams, S.: A comprehensive artificial intelligence framework for dental diagnosis and charting. BMC Oral Health 22(1), 1–13 (2022)
  * Brahmi and Jdey [2023] Brahmi, W., Jdey, I.: Automatic tooth instance segmentation and identification from panoramic x-ray images using deep cnn. Multimedia Tools and Applications, 1–21 (2023)
  * Sivasundaram and Pandian [2021] Sivasundaram, S., Pandian, C.M.: Performance analysis of classification and segmentation of cysts in panoramic dental images using convolutional neural network architecture. International Journal of Imaging Systems and Technology 31, 2214–2225 (2021)
  * Krois et al. [2021] Krois, J., Cantu, A.G., Chaurasia, A., Patil, R.N., Chaudhari, P.K., Gaudin, R.A., Gehrung, S., Schwendicke, F.: Generalizability of deep learning models for dental image analysis. Scientific Reports 11 (2021)
  * Ekert et al. [2019] Ekert, T., Krois, J., Meinhold, L., Elhennawy, K., Emara, R., Golla, T., Schwendicke, F.: Deep learning for the radiographic detection of apical lesions. Journal of endodontics (2019)
  * Franco et al. [2022] Franco, A., Porto, L., Heng, D., Murray, J., Lygate, A., Franco, R., Bueno, J., Sobania, M., Costa, M.M., Paranhos, L.R., et al.: Diagnostic performance of convolutional neural networks for dental sexual dimorphism. Scientific Reports 12(1), 17279 (2022)
  * Mohammad et al. [2022] Mohammad, N., Muad, A.M., Ahmad, R., Yusof, M.Y.P.M.: Accuracy of advanced deep learning with tensorflow and keras for classifying teeth developmental stages in digital panoramic imaging. BMC Medical Imaging 22(1), 66 (2022)
  * Vila-Blanco et al. [2022] Vila-Blanco, N., Varas-Quintana, P., Aneiros-Ardao, Á., Tomás, I., Carreira, M.J.: Xas: Automatic yet explainable age and sex determination by combining imprecise per-tooth predictions. Computers in Biology and Medicine 149, 106072 (2022)
  * Rajee and Mythili [2021] Rajee, M.V., Mythili, C.: Gender classification on digital dental x-ray images using deep convolutional neural network. Biomed. Signal Process. Control. 69, 102939 (2021)
  * Zhu et al. [2022] Zhu, H., Cao, Z., Lian, L., Ye, G., Gao, H., Wu, J.: Cariesnet: a deep learning approach for segmentation of multi-stage caries lesion from oral panoramic x-ray image. Neural Computing and Applications, 1–9 (2022)
  * Park et al. [2022] Park, E.Y., Cho, H., Kang, S., Jeong, S., Kim, E.-K.: Caries detection with tooth surface segmentation on intraoral photographic images using deep learning. BMC Oral Health 22(1), 1–9 (2022)
  * Imak et al. [2022] Imak, A., Celebi, A., Siddique, K., Turkoglu, M., Sengur, A., Salam, I.: Dental caries detection using score-based multi-input deep convolutional neural network. IEEE Access 10, 18320–18329 (2022)
  * Hsiao et al. [2021] Hsiao, T.-Y., Ho, Y.-C., Chen, M.-R., Lee, S.-Y., Sun, C.: Disease activation maps for subgingival dental calculus identification based on intelligent dental optical coherence tomography. Translational Biophotonics 3 (2021)
  * Liu et al. [2022] Liu, M.-Q., Xu, Z.-N., Mao, W.-Y., Li, Y., Zhang, X.-H., Bai, H.-L., Ding, P., Fu, K.-Y.: Deep learning-based evaluation of the relationship between mandibular third molar and mandibular canal on cbct. Clinical Oral Investigations, 1–11 (2022)
  * Zhao et al. [2020] Zhao, Y., Li, P., Gao, C., Liu, Y., Chen, Q., Yang, F., Meng, D.: Tsasnet: Tooth segmentation on dental panoramic x-ray images by two-stage attention segmentation network. Knowl. Based Syst. 206, 106338 (2020)
  * Kwak et al. [2020] Kwak, G.H., Kwak, E.-J., Song, J.M., Park, H.R., Jung, Y.-H., Cho, B.-H., Hui, P., Hwang, J.: Automatic mandibular canal detection using a deep convolutional neural network. Scientific Reports 10 (2020)
  * Tian et al. [2019] Tian, S., Dai, N., Zhang, B., Yuan, F., Yu, Q., Cheng, X.: Automatic classification and segmentation of teeth on 3d dental model using hierarchical deep learning networks. IEEE Access 7, 84817–84828 (2019)
  * Bonfanti-Gris et al. [2022] Bonfanti-Gris, M., Garcia-Canas, A., Alonso-Calvo, R., Rodriguez-Manzaneque, M.P.S., Ramiro, G.P.: Evaluation of an artificial intelligence web-based software to detect and classify dental structures and treatments in panoramic radiographs. Journal of Dentistry 126, 104301 (2022)
  * Oztekin et al. [2022] Oztekin, F., Katar, O., Sadak, F., Aydogan, M., Yildirim, T.T., Plawiak, P., Yildirim, O., Talo, M., Karabatak, M.: Automatic semantic segmentation for dental restorations in panoramic radiography images using u-net model. International Journal of Imaging Systems and Technology 32(6), 1990–2001 (2022)
  * Kohlakala et al. [2022] Kohlakala, A., Coetzer, J., Bertels, J., Vandermeulen, D.: Deep learning-based dental implant recognition using synthetic x-ray images. Medical & Biological Engineering & Computing 60(10), 2951–2968 (2022)
  * Başaran et al. [2021] Başaran, M., Çelik, Ö., Bayrakdar, I.S., Bilgir, E., Orhan, K., Odabaş, A., Aslan, A.F., Jagtap, R.: Diagnostic charting of panoramic radiography using deep-learning artificial intelligence system. Oral Radiology 38, 363–369 (2021)
  * Lee et al. [2022] Lee, C.-T., Kabir, T., Nelson, J., Sheng, S., Meng, H.-W., Van Dyke, T.E., Walji, M.F., Jiang, X., Shams, S.: Use of the deep learning approach to measure alveolar bone level. Journal of clinical periodontology 49(3), 260–269 (2022)
  * Chen et al. [2021] Chen, H., Li, H., Zhao, Y., Zhao, J.-j., Wang, Y.: Dental disease detection on periapical radiographs based on deep convolutional neural networks. International Journal of Computer Assisted Radiology and Surgery 16, 649–661 (2021)
  * Kheraif et al. [2019] Kheraif, A.A.A., Wahba, A.A., Fouad, H.: Detection of dental diseases from radiographic 2d dental image using hybrid graph-cut technique and convolutional neural network. Measurement (2019)
  * Park and Lee [2022] Park, J., Lee, Y.: Oriented-tooth recognition using a five-axis object-detection approach. Applied Intelligence, 1–12 (2022)
  * Gheisari et al. [2023] Gheisari, M., Ebrahimzadeh, F., Rahimi, M., Moazzamigodarzi, M., Liu, Y., Dutta Pramanik, P.K., Heravi, M.A., Mehbodniya, A., Ghaderzadeh, M., Feylizadeh, M.R., et al.: Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey. CAAI Transactions on Intelligence Technology (2023)
  * Wang et al. [2019] Wang, Z., Liu, K., Li, J., Zhu, Y., Zhang, Y.: Various frameworks and libraries of machine learning and deep learning: a survey. Archives of computational methods in engineering, 1–24 (2019)
  * Murad et al. [2023] Murad, M.A.A., Khalid, F., Manshor, N., Al-Jumaily, A., et al.: Medical image analysis of lung tumor diagnosis based on generalized fuzzy c-means clustering algorithm. In: Journal of Physics: Conference Series, vol. 2467, p. 012004 (2023). IOP Publishing
  * Paskze and Chintala [2017] Paskze, A., Chintala, S.: Tensors and Dynamic neural networks in Python with strong GPU acceleration. Jan (2017)
  * Jia et al. [2014] Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678 (2014)
  * MXNet and MXNet [2018] MXNet, A., MXNet, A.: A flexible and efficient library for deep learning. The Apache Software Foundation (ASF) (2018)
  * LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)
  * Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  * Szegedy et al. [2015] Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
  * Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
  * Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015)
  * Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pp. 234–241 (2015). Springer
  * He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
  * Howard et al. [2017] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)
  * Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
  * Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
  * He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)
  * Choi et al. [2023] Choi, E., Pang, K., Jeong, E., Lee, S., Son, Y., Seo, M.-S.: Artificial intelligence in diagnosing dens evaginatus on periapical radiography with limited data availability. Scientific Reports 13(1), 13232 (2023)
  * Chun et al. [2023] Chun, S.-Y., Kang, Y.-H., Yang, S., Kang, S.-R., Lee, S.-J., Kim, J.-M., Kim, J.-E., Huh, K.-H., Lee, S.-S., Heo, M.-S., Yi, W.-J.: Automatic classification of 3D positional relationship between mandibular third molar and inferior alveolar canal using a distance-aware network. BMC Oral Health 23(1), 794 (2023)
  * Amasya et al. [2023] Amasya, H., Jaju, P.P., Ezhov, M., Gusarev, M., Atakan, C., Sanders, A., Manulius, D., Golitskya, M., Shrivastava, K., Singh, A., et al.: Development and validation of an artificial intelligence software for periodontal bone loss in panoramic imaging. International Journal of Imaging Systems and Technology (2023)
  * Ali et al. [2023] Ali, M.A., Fujita, D., Kobashi, S.: Teeth and prostheses detection in dental panoramic x-rays using cnn-based object detector and a priori knowledge-based algorithm. Scientific Reports 13(1), 16542 (2023)
  * Müller et al. [2022] Müller, D., Soto-Rey, I., Kramer, F.: Towards a guideline for evaluation metrics in medical image segmentation. BMC Research Notes 15(1), 1–8 (2022)
  * Hanley and McNeil [1982] Hanley, J.A., McNeil, B.J.: The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology 143(1), 29–36 (1982)
  * Taha and Hanbury [2015] Taha, A.A., Hanbury, A.: Metrics for evaluating 3d medical image segmentation: analysis, selection, and tool. BMC medical imaging 15(1), 1–28 (2015)
  * Wang et al. [2020] Wang, Z., Wang, E., Zhu, Y.: Image segmentation evaluation: a survey of methods. Artificial Intelligence Review 53, 5637–5674 (2020)
  * Silva et al. [2018] Silva, G., Oliveira, L., Pithon, M.: Automatic segmenting teeth in x-ray images: Trends, a novel data set, benchmarking and future perspectives. Expert Systems with Applications 107, 15–31 (2018)
  * Abdi [2017] Abdi, S. Amir; Kasaei: “Panoramic Dental X-rays With Segmented Mandibles”, Mendeley Data, V1, doi: 10.17632/hxt48yk462.1. ”https://data.mendeley.com/datasets/hxt48yk462/1” (2017)
  * Walid and Imen [2023] Walid, B., Imen, J.: Panoramic Dental Xray Dataset. Mendeley (2023)
  * Panetta et al. [2021] Panetta, K., Rajendran, R., Ramesh, A., Rao, S.P., Agaian, S.: Tufts dental database: a multimodal panoramic x-ray dataset for benchmarking diagnostic systems. IEEE journal of biomedical and health informatics 26(4), 1650–1659 (2021)
  * Zhang et al. [2023] Zhang, Y., Ye, F., Chen, L., Xu, F., Chen, X., Wu, H., Cao, M., Li, Y., Wang, Y., Huang, X.: Children’s dental panoramic radiographs dataset for caries segmentation and dental disease detection. Scientific Data 10(1), 380 (2023)