Predicting Parkinson’s disease evolution using deep learning.

Maria Frasca1,*, Davide La Torre2, Gabriella Pravettoni1 Ilaria Cutica1,

1 Department of Oncology and Hemato-Oncology, University of Milan, Italy
2 SKEMA Business School, Univerisité Côte d’Azur, Sophia Antipolis, France

*<EMAIL_ADDRESS>
## Abstract

Parkinson’s disease (PD) is a neurological condition that occurs in nearly 1%
of the world’s population. The disease is manifested by a sharp drop in
dopamine production, symptoms are cognitive and behavioural and include a wide
range of personality changes, depressive disorders, memory problems, and
emotional dysregulation, which can occur as the disease progresses. Early
diagnosis and accurate staging of the disease are essential to apply the
appropriate therapeutic approaches to slow cognitive and motor decline.

Currently, there is not a single blood test or biomarker available to diagnose
Parkinson’s disease. Magnetic resonance imaging (MRI) has been used for the
past three decades to diagnose and distinguish between PD and other
neurological conditions. However, in recent years new possibilities have
arisen: several AI algorithms have been developed to increase the precision
and accuracy of differential diagnosis of PD at an early stage.

To our knowledge, no AI tools have been designed to identify the stage of
progression. This paper aims to fill this gap. Using the ”Parkinson’s
Progression Markers Initiative” dataset, which reports the patient’s MRI and
an indication of the disease stage, we developed a model to identify the level
of progression. The images and the associated scores were used for training
and assessing different deep-learning models. Our analysis distinguished four
distinct disease progression levels based on a standard scale (Hoehn and Yah
scale). The final architecture consists of the cascading of a 3DCNN network,
adopted to reduce and extract the spatial characteristics of the RMI for
efficient training of the successive LSTM layers, aiming at modelling the
temporal dependencies among the data. Our results show that the proposed 3DCNN
+ LSTM model achieves state-of-the-art results by classifying the elements
with 91.90% as macro averaged OVR AUC on four classes

## Introduction

Parkinson’s disease (PD) is a neurological condition that is seen more
frequently in the elderly. Nearly 1% of the world population is affected by PD
and several affected patients have significant physical and cognitive
problems[41]. The disease is manifested by a sharp drop in dopamine
production, resulting from the death of the related producing cells present in
an area called the substantia nigra present in the midbrain[16]. The disease
most likely spreads throughout the brain via a protein called alpha-synuclein,
which is found in the region between the spinal cord and the brain[81].
Symptoms are both cognitive and behavioural, and include a wide range of
personality changes, depressive disorders, memory problems, and emotional
dysregulation, which can occur as the disease progresses [55]. Also, the
symptoms related to mobility worsen as the disease progresses. Early diagnosis
is essential to apply the appropriate therapeutic approaches to slow cognitive
decline. Currently, a series of tests are needed to diagnose or verify the
progression of Parkinson’s, as today we are still looking for a biomarker for
a quick diagnosis [24, 31].

Clinicians identify the disease by analyzing the patient’s symptoms, which
include slow movement, stiffness, tremors, and problems with balance and
coordination. However, these symptoms and the speed at which they progress can
vary from case to case [46].

Magnetic Resonance Imaging (MRI), a non-invasive medical technology that
produces images of the internal anatomy of the body, has been used for the
past three decades to diagnose and distinguish between PD and other
neurological conditions [33, 40]. Furthermore, it has also been used in
clinical trials to evaluate the efficacy of therapies [12].

Many clinical studies have tried to detect PD, through different
classification methods [66, 82, 59] and on different types of data, ranging
from physiological signals, such as speech [39] and EEG [8] to gait [80],
handwriting [63], and neuroimaging [84] analysis. In particular, neuroimaging
analysis has been successfully applied in various sophisticated methods for PD
diagnosis[66]. A key challenge is to construct quantitative models of
pathological and cognitive decline in PD progression using patient data such
as neuroimaging and clinical measures for several reasons, including[5]:

  * •

To perform an accurate diagnosis. Understanding the progression of PD can help
doctors diagnose the disease earlier and more accurately, allowing treatment
to be started as soon as possible. This can improve the patient’s quality of
life and delay the progression of symptoms.

  * •

To improve symptom management. A deeper understanding of PD progression can
help clinicians determine the most appropriate and personalized treatment for
the patient. This can improve symptom control, allowing patients to maintain
an optimal level of functioning for as long as possible.

  * •

To develop new treatments. Understanding the progression of PD can help
researchers identify new therapeutic targets and develop new treatments. This
may lead to the discovery of new drugs and therapies to improve patient’s
quality of life and slow the progression of the disease.

  * •

To provide information to patients and their families. Understanding the
progression of PD can help patients and their families prepare for what may
happen and take preventative steps to reduce the risk of complications. It can
also help patients manage their expectations about the course of their disease
and make informed decisions about their treatment and care.

The knowledge of the precise sequence of events in the progression of PD is
important for:

  * •

Reducing heterogeneity in clinical trials;

  * •

Monitoring the outcome of the treatment for new therapeutic interventions;

  * •

Providing important information about the degeneration mechanism of PD.

This paper proposes a methodology to classify the different stages of PD. To
date, several AI tools have been developed to diagnose the illness; none of
them, however, have been created to determine the disease’s progression stage.
This paper aims to cover this gap by creating a model to determine the degree
of progression using the ”Parkinson’s Progression Markers Initiative” (PPMI)
dataset, which provides the patient’s MRI and an indicator of the illness
stage. We experiment with deep neural network models trained using the PPMI
dataset containing MRI images, relating to several follow-up visits over time
for the same patients. The dataset associates also each MRI image with the
Hoehn and Yahr (H&Y) scale [34], an internationally used PD progression rating
method for clinical practice providing a score of the Parkinson’s disease
severity. We used them as a label for MRI images [75].

Different pre-processing techniques have been applied to the MRI images in the
dataset. Subsequently, to have sufficient elements to train a neural network,
new images were generated through a data augmentation process. The
experimented neural network was composed of a 3D-CNN to extract the
characteristics of the three-dimensional images, to which we added one or more
recurrent layers (GRU or LSTM) to analyze the temporal data.

The paper is structured as follows: Section Related Works illustrates the
studies and works related to the proposed research. Section The Dataset
describes the Parkinson’s Progression Markers Initiative dataset, and how it
was used to derive the dataset used in this study. Section Methodology
presents the data processing and augmentation techniques, and describes the
selected neural network models, while Section 1 discusses the results.
Finally, Section Conclusion and Future Work concludes and presents future
developments of this work.

## Parkinson’s Disease Background

Early recognition of PD is of great importance and can positively influence
the patient’s treatment and quality of life. Recognizing PD at an early stage
allows treatment to be started promptly. While there is no definitive cure,
there are pharmacological and non-pharmacological therapies that can help
manage symptoms and improve quality of life. Some drugs used to treat PD are
more effective in the early stages of the disease. Therefore, identifying the
condition at an early stage can increase the likelihood of a positive response
to treatment. Furthermore, more effective symptom management strategies can be
applied, for example through physical, occupational and speech therapies.
These therapies can help maintain mobility, improve communication, and address
any cognitive challenges.

PD is a brain disease that causes involuntary or uncontrolled movements, such
as tremors, rigidity, bradykinesia, and difficulty with balance and
coordination. Symptoms usually begin gradually and worsen over time, affecting
the ability to walk, talk, and carry out normal daily activities. Motor
symptoms are usually accompanied by mental and behavioural changes, sleep
problems, depression, memory difficulties and fatigue [76]. Although anyone
can be at risk of developing PD, some research suggests that it affects men
more frequently than women. Age is a significant risk factor, with most people
developing the disease after age 60. However, approximately 5-10% of cases
have an onset before the age of 50, and in some of these cases, the disease
may be hereditary [21].

The causes of the onset of PD are not yet completely known, but being a
multifactorial disease, causes may include:

  * •

_Damage to nerve cells:_ In PD, there is deterioration and sometimes death of
nerve cells in the part of the brain called the basal ganglion, an area
involved in the control of movements [10].

  * •

_Dopamine deficiency:_ Nerve cells in the basal ganglion normally produce a
chemical called dopamine, but in PD the death or damage of these cells causes
a deficiency of dopamine.

  * •

_Presence of Lewy body:_ Many patients develop abnormal aggregates of proteins
called Lewy bodies in their brain cells. These aggregates can interfere with
the normal function of nerve cells [67].

  * •

_Genetic factors:_ While most cases of PD are not hereditary, there are cases
in which the disease is linked to specific genetic variants. Numerous genes,
including SNCA, Parkin, UCHL1, DJ-1, PINK-1, LRRK2, and ATP13A2, have
mutations that cause familial forms of PD [62]. However, genetics appears to
play a limited role in most cases.

  * •

_Environmental factors:_ There are indications that exposure to certain
environmental factors could increase the risk of developing Parkinson’s
disease. These factors may include exposure to environmental toxins or certain
chemicals [57].

There are currently no specific laboratory tests to diagnose PD, and the
diagnosis is often based on the patient’s medical history and neurological
tests. If symptoms improve with medication, this may confirm the diagnosis.
Some disorders can cause symptoms similar to PD, so it is important to rule
out other possible causes [52].

While there is no cure for Parkinson’s disease, there are treatments that can
relieve symptoms. Drugs are often prescribed to increase dopamine levels in
the brain or affect other neurotransmitters. Other therapies, such as deep
brain stimulation, may be considered in severe or drug-resistant cases [17].

PD is a complex condition that goes beyond its obvious motor symptoms. It has
a profound impact on the psychological sphere of the individuals involved. The
presence of debilitating motor symptoms, such as tremors and muscle stiffness,
can trigger depressive and anxious moods. Awareness of disease progression and
daily challenges can contribute to feelings of isolation and reduced
psychological quality of life [19].

Quality of life becomes a struggle for independence and social participation.
The loss of autonomy in movement is more than a physical challenge; it’s a
psychological battle that can lead to feelings of frustration and isolation,
and the psychological aspect plays a crucial role in how people cope with this
disease. Cognitive-behavioral therapy can be an integral part of the treatment
process. These approaches not only address specific psychological issues but
also promote resilience and overall emotional well-being.

### Hoehn and Yahr Scale

The most widely used tool for tracking PD progression, although combined with
other clinical evaluations, is the Hoehn and Yahr scale (H&Y Scale [34],) [7].
This scale offers a framework for evaluating PD development, which is
essential for clinical care and to get a clear understanding of how the
disease affects various facets of everyday life. The proposed stages of the
Hoehn and Yahr Scale are:

  * •

_Initial stage (1-2)._ In the early stages, symptoms are often mild and may
affect only one side of the body (unilateral). In _stage 1_ , symptoms are
subtle and may not significantly affect daily life. In _stage 2_ , bilateral
involvement begins to appear, but the posture is still preserved.

  * •

_Intermediate stages (3-4)._ In _stage 3_ , impaired posture becomes evident,
and the patient may begin to experience occasional falls. In the _stage 4_ ,
symptoms are more severe, with greater dependence on assistance to carry out
daily activities. Posture is significantly compromised.

  * •

_Advanced Stage (5)._ In the _stage 5_ and final stage, the disease reaches an
advanced point with severe motor symptoms. Dependence on assistance is
maximum, and the patient may require full-time care. Walking without
assistance becomes impossible.

However, Hoehn and Yahr’s scale only considers the motor symptoms of PD,
ignoring several non-motor symptoms and psychosocial factors that can have a
substantial impact on patients’ quality of life. Additionally, as Parkinson’s
symptoms may be heterogeneous, each person’s rate of progression might be
different. The scale provides a broad overview of the intensity of the motor
symptoms, but effective clinical therapy necessitates a more thorough
evaluation of non-motor symptoms, the overall course of the illness, and the
influence on the patient’s day-to-day activities.

## Deep Learning Architectures: A Review

###  0.1 Convolutional Neural Network

Convolutional Neural Networks (CNNs) are a class of deep neural networks that
are commonly used in image analysis and recognition [65]. They are
particularly good at tasks like image classification, object detection and
segmentation, image filtering, and feature extraction. Convolution layers take
as input either an image or the output of other layers and they create feature
maps. To do this, a kernel that is automatically trained by the network is
used. In general, CNNs have layers organized hierarchically, so that the
deeper the layer the more precise the attributes manipulated by its
architecture.

A 3D CNN extends the CNN concept to three-dimensional data, such as video data
or medical imaging data with depth information that can be processed.
Traditional CNNs operate on 2D data such as images, where convolutional
operations are performed in two dimensions (height and width). In contrast, 3D
CNNs can process spatial and temporal information simultaneously, making them
suitable for tasks involving sequences of 3D data, such as video frames over
time. This labour-intensive approach currently requires specialized analysis
[45, 49].

The application of 3D CNNs involves considering volumes of data rather than
individual images. Every convolutional layer can capture and learn spatial and
temporal patterns from the input data as well as execute three-dimensional
convolution operations. This method has been demonstrated to be especially
successful in situations where spatial depth or temporal sequence is critical,
including tracing moving objects or making diagnoses based on medical imaging
[72]. The architecture of a 3D CNN includes:

3D Convolutional layers:

$y(i,j,k)=\sum_{l=0}^{L-1}\sum_{m=0}^{M-1}\sum_{n=0}^{N-1}x(i+l,j+m,k+n)*w(l,m,n)+b$

where:

  1. 1.

$y(i,j,k)$ is the value of the resulting feature map at the location
$(i,j,k)$.

  2. 2.

$x(i+l,j+m,k+n)*w(l,m,n)$ are the values of the input at the location
$(i+l,j+m,k+n)*w(l,m,n)$.

  3. 3.

$w(l,m,n)$ are the weights of the convolution kernel.

  4. 4.

$b$ is the bias.

Pooling layers:

$y(i,j,k)=poolingfunction(x_{i,j,k},x_{i+1,j,k},...,x_{i+P-1,j+Q,k+R-1})$

where:

  1. 1.

$y(i,j,k)$ is the resulting value of the pooling layer,

  2. 2.

$P,Q,R$ is the size of the pooling window,

  3. 3.

$x_{i,j,k}$ are the values of the input in the pooling region.

Fully connected layers:

$y=\sigma\left(\sum_{i=1}^{N}w_{i}*x_{i}+b\right)$

where:

  1. 1.

$y$ is the output of the fully connected layer,

  2. 2.

$x_{i}$ they are the outputs of the previous units,

  3. 3.

$w_{i}$ are the weights associated with each input,

  4. 4.

$b$: is the bias,

  5. 5.

$\sigma$ is the activation function.

These formulas describe the fundamental operations in 3D-CNN, namely 3D
convolution for feature extraction, 3D pooling for downsampling and fully
connected layers for final classification. Convolutional operations are
performed in three dimensions (width, height, and depth) to capture spatio-
temporal features.

### Recurrent Neural Network

Recurrent Neural Networks (RNN) are suitable for sequential data or time
series data [85]. These networks are commonly used for ordinary or temporal
problems, such as translation, language processing (NLP), speech recognition,
and image subtitling. Like CNN, RNNs use training data to learn. Their
importance relies on the ”memory” property as the output of the current input
depends on past computations [42]. While traditional deep neural networks
assume that inputs and outputs are independent, the output of RNNs depends on
the preceding elements within the sequence 111https://www.ibm.com/it-
it/topics/recurrent-neural-networks. RNNs can also be stacked and used to
learn temporal information in both directions, and in this case, we refer to
them as bidirectional RNNs (BRNNs). Their most important property consists in
being able to make predictions using both past and future data [32].

Among RNNs, there are two very popular architectures: the Long Short-Term
Memory (LSTM) and the Gated Recurrent Unit (GRU).

#### Long Short-Term Memory

LSTMs were created in response to the issue of gradient disappearance in
conventional neural networks. Gradient disappearance can happen when a neural
network’s inability to learn long-term associations is caused by incorrect
backpropagation during training, which results in very small gradients. LSTMs
were introduced to address this issue and enable neural networks to record
long-range temporal dependencies [71].

They introduce the concept of ”gate”, which allows for control of the flow of
information necessary to predict the output in the network through operations
on matrices. There are three types of gates in LSTMs as shown in Fig. 1.

Input Gate: it decides which new information will be stored in the memory
cell, and it is described by:

$i_{t}=\sigma(W_{ii}*x_{t}+b_{ii}+W_{hi}*h_{t-1}+b_{hi})$
$g_{t}=\tanh(W_{ig}*x_{t}+b_{ig}+W_{hg}*h_{t-1}+b_{hg})$ $a_{t}=i_{t}*g_{t}$

Forget Gate: it decides which information in the memory cell should be deleted
or forgotten, and it is characterized by the following relations:

$f_{t}=\sigma(W_{if}*x_{t}+b_{if}+W_{hf}*h_{t-1}+b_{hf})$
$o_{t}=\tanh(W_{og}*x_{t}+b_{og}+W_{hg}*h_{t-1}+b_{hg})$ $a_{t}=f_{t}*g_{t-1}$

Output Gate: it calculates the output based on the current memory cell and the
current input through the following expressions:

$o_{t}=\sigma(W_{io}*x_{t}+b_{io}+W_{ho}*h_{t-1}+b_{ho})$
$h_{t}=o_{t}*\tanh(c_{t})$

Furthermore, in LSTMs, we also have _Cell State Update_ which is responsible
for updating the cell state based on the information received from the input
and forget gates. The formula for updating the cell state in LSTMs is as
follows:

$c_{t}=f_{t}*c_{t-1}+a_{t}$

where the different symbols indicate:

  * •

$x_{t}$ is the current input.,

  * •

$h_{t-1}$ is the output of the LSTM cell at the previous time step,

  * •

$W$ and $b$ are the weights and biases of the model,

  * •

$\sigma$ is the sigmoid function,

  * •

$\tanh$ is the hyperbolic tangent function.

These formulas describe the flow of information through an LSTM cell. The cell
stores a state $c_{t}$ which is updated or forgotten based on the input,
forget, and output ports. The output $h_{t}$ is then calculated based on the
information in the cell state, and then filtered through the output gate [25].

Figure 1: An example of an LSTM architecture

#### Gated Recurrent Units

GRUs are another variant of RNN designed to address the limitations of
traditional neural networks in modelling complex temporal sequences. GRUs are
structured in a simpler way than LSTMs [14] as they use only two gates as
shown in Fig. 2:

Reset Gate: This gate decides how much of past information should be erased
and it controls how much of past information should be mixed with new
information.

$z_{t}=\sigma(W_{z}*x_{t}+U_{t}*h_{t-1}+b_{z})$

Update Gate: This gate determines how much past information should be added to
the existing information stored in the memory cell.

$r_{t}=\sigma(W_{r}*x_{t}+U_{r}*h_{t-1}+b_{r})$

In GRUs, we also have the notion of _Candidate Hidden State_ and it is
calculated as a weighted combination between the output of a reset gate
$(r_{t})$ and the previous hidden state $h_{t-1}$, along with the current
input $x_{t}$, using the following formula:

$\overline{h_{t}}=tanh(W*x_{t}+U*(r_{t}\odot h_{t-1})+b)$

Then, the _Hidden State Update_ is performed using a weighted update operation
between the candidate hidden state and the previous hidden state. Below is the
formula:

$h_{t}=(1-z_{t})\odot h_{t-1}+z_{t}\odot\overline{h_{t}}$

The different symbols indicate:

  * •

$c_{t}$ is the state of the cell at the time $t$,

  * •

$f_{t}$ is the output of the forget gate,

  * •

$c_{t-1}$ is the state of the cell at the previous time $t-1$,

  * •

$i_{t}$ is the output of the input gate,

  * •

$g_{t}$ is the output of the candidate gate,

  * •

$W,U,b$ are the weights and biases of the model,

  * •

$\odot$ denotes the element-wise product

These formulas describe how the candidate hidden state $\overline{h_{t}}$ is
calculated and how the hidden state $h_{t}$ is updated using information from
the hidden state candidate and the update gate $z_{t}$. The update gate
governs how much of the new candidate’s hidden state should replace the
previous hidden state.

Unlike LSTMs, GRUs do not have a separate forget gate. The lack of this gate
simplifies the structure of GRUs, making them faster to be trained and less
computationally expensive than LSTMs.

Figure 2: An example of a GRU architecture

## Related Works

Extensive research has been conducted for a long time in the medical field on
clinical, cognitive, and biomarker neuroimaging datasets for normal ageing and
Parkinson’s disease [66]. Several models and techniques of Artificial
Intelligence have been applied to the medical field and in particular, in the
area of neuroimaging for detecting PD. In this section, we discuss related
work adopting AI techniques for supporting both PD diagnosis and
classification of the progression level.

Vyas et al. [79] used MRI images to diagnose PD. They also identified
biomarkers that can be used to observe how PD spreads. A total of 318 MRI
scans from the Parkinson’s Progressions Markers Initiative (PPMI) were
utilized to create the dataset and to train and test two models: a 2D CNN and
a 3D CNN. the 3D model is more accurate, reaching 88.9% classification
accuracy.

Butt et al. [13] assessed the capacity of the Leap Motion Controller, a device
able to measure positional data, to detect PD by evaluating the motor
impairment of patients. To this aim, they recruited 16 subjects with
Parkinson’s disease and 12 healthy individuals and performed data acquisition
from the leap motion of both samples. They assessed the performance of three
classification models: logistic regression, Naïve Bayes, and Support Vector
Machine. The best performance was reached by logistic regression (70.37%
accuracy).

Templeton et al. [75] adopted Machine Learning (ML) to detect the PD level on
the basis of digital neurocognitive assessments (e.g., digital biomarkers)
consisting of several tests on a tablet. In this study, 75 participants — 50
people with PD and 25 controls — took part in functional movement evaluations,
standardized health questionnaires, and 14 tablet-based neurocognitive
functioning tests. The PD patients are classified according to the H&Y scale.
Decision tree classification of sensor-based characteristics enabled the
differentiation of motor dysfunction in patients with PD from healthy controls
with an accuracy of 92.6% and the early and advanced phases of PD with an
accuracy of 73.70%.

Ferreira et al. [22] performed gait analysis to support both the PD detection
and the classification of the PD phases considering spatio-temporal parameters
of walking features, such as variability and asymmetry using wearable sensors.
42 people with mild and 21 people with moderate PD make up the dataset. The
tests examined gait data collected from PD patients with varying degrees of PD
motor symptom severity and 63 matched control group participants. The authors
performed the classification between PD and non-PD patients reaching an
accuracy rate of 84.6% with the Naïve Bayes algorithm. The progression level
has been measured by adopting the H&Y classification. The best PD progression
classification performance is reached by the Random Forest algorithm, by
reaching an average Area Under the ROC curve of 0.786. In our case, with an
RMI analysis, we reached an average Area Under the ROC curve of 91.90%.

Veetil et al. [78] applied and evaluated the use of pre-trained models
AlexNet, Xception, DenseNet201, ResNet50, VGG16 and VGG19 for Parkinson’s
classification. They also adopted the PPMI dataset. Using pre-trained models
with images in two dimensions from the ImageNet dataset, VGG19 obtained an
accuracy of 92.6%.

In [56] 123I-ioflupane thermal brain (SPECT) and 123I-ioflupane positron
emission tomography (PET) images were taken from PPMI and analyzed to
discriminate Parkinson’s patients from controls. The features selected to
train linear (Linear Regression, Quadratic Discriminant Analysis) and
nonlinear (Support Vector Machine - SVM, Random Forest, neural network)
classifiers were shape indices of the segmented contours (ellipticity,
compactness, roughness, etc.). SVM achieved 97% accuracy. The participants in
the study were 208 healthy normals, 427 PD, and 80 SWEDD, i.e., individuals
with mutation of the DYT11 gene in patients without dopaminergic deficiency.

Adeli et al. [1] proposed the Joint Feature-Sample Selection (JFSS) method, a
classification framework for simultaneously decluttering the selected subset
of features and samples and learning a classification model. To evaluate the
proposed method, they used the PPMI and ADNI datasets. They considered T1 MRI
collections of Parkinson’s patients and controls, by extracting volumetric and
shape characteristics from different brain regions. Data were normalized to
reduce anatomic-functional variations. Noise and outliers were removed. The
selected features were used to train linear and nonlinear classifiers. Results
indicated that the proposed method can identify imaging biomarkers and
diagnose the disease with an accuracy of 81.9% using robust Linear
Discriminant Analysis.

Peng et al.[54] proposed a multilevel Region-Of-Interest feature-based machine
learning method that uses brain T1 MRI images from the PPMI dataset (69 PD
patients and 103 normal controls) to detect PD-related morphometric biomarkers
and improve disease identification power. The feature selection method based
on filters and wrappers and the multi-kernel SVM are used in the
classification algorithm and achieved an accuracy of 85.78%.

In [69] the voice of PD patients is analyzed to predict the Motor and Total-
UPDRS (Unified Parkinson’s Disease Rating Scale) scores. The voice
measurements were captured automatically from the patient’s home for 6 months.
The dataset used was taken from the machine learning repository provided by
the University of California at Irvine (UCI). Multicollinearity problems in
the data set are addressed and the input feature space is made smaller using
principal component analysis (PCA). They applied a neural network to the
condensed input feature space, achieving for the Motor-UPDRS a coefficient of
determination 97% and 94.8% in the test and validation phases, respectively.
Concerning the Total-UPDRS, the coefficient of determination was 95.6% and
92.9% in the test and validation phases, respectively.

In [18] Dadu et al. used machine learning methods (Random forest, LightGBM,
XGBoost, SVM, Clustering and Lasso-regression models) on complete clinic
longitudinal data from the PPMI (n = 294 cases) to identify patient subtypes
and predict the progression of the illness. The resulting models were
validated in an independent, well-defined clinical panel (n = 263 cases). The
analysis identified three distinct subtypes, which correspond to a slow,
moderate, and rapid progression of the illness. Five years after the initial
diagnosis, they obtained extremely accurate predictions of the malady’s
progression, with AUC values of 0,92 for the group experiencing the slower
progression, 0,87 for the moderate advancement, and 0,95 for the group
experiencing the faster progression.

García-Ordás et al. [26] proposed a method based on deep learning techniques
for early diagnosis and severity prediction of Parkinson’s disease through
voice analysis. The dataset used consists of data extracted from the speech
analysis of patients, with the use of an autoencoder to intelligently reduce
the features. The work is divided into two steps: i) classification to
determine the severity of Parkinson’s disease; and ii) regression to predict
the degree of disease evolution through UPDRS. The proposed model is a mixed
multi-layer perceptron, capable of performing classification and regression
simultaneously. The results show a success rate of 99.15% in classifying
severity and a root mean square error of 0.15 in predicting the degree of
disease involvement.

Severson et al.[68] proposed a study that focuses on creating a statistical
progression model for Parkinson’s disease that takes into account intra-
individual and inter-individual variability and drug effects. Using
longitudinal data from 423 early-stage Parkinson’s patients and 196 healthy
controls collected for up to 7 years, the model identifies eight disease
states based on motor and non-motor symptoms. The most notable finding is the
discovery of overlapping and non-sequential progression trajectories,
challenging the assumption of deterministic progression patterns across
disease subclasses. This approach provides a comprehensive view of PD
progression, highlighting the heterogeneous nature of the disease and
suggesting that static subtype assignment may not be effective in capturing
the full spectrum of PD progression. The model could have significant
implications in the management of PD patients and the design of clinical
trials, serving as a prognostic and predictive tool.

Most of the works proposed in the literature deal with recognising
Parkinson’s. However, it is also important to identify the level of PD. For
this reason, we propose an approach to identify the level of progression of
Parkinson’s disease in patients by examining their MRIs. Determining disease
staging is crucial because it can be used to predict future illness
development and identify people who might benefit from early therapeutic
interventions.

This work differs from the others because we consider a time series of MRI
images to train a multi-class classifier. Thus, this classifier learns to
identify the differences between images of different PD levels according to
the H&Y scale. We use RNN+CNN hybrid neural networks. The first network during
the training examines the images of the time series while the second analyzes
each single image in detail.

## The Dataset

The dataset adopted is the ”Parkinson’s Progression Markers Initiative” (PPMI)
[48], launched in 2010 to identify biomarkers of the onset and progression of
Parkinson’s disease. PPMI is mainly sponsored by ”The Michael J. Fox
Foundation for Parkinson’s Research”, a foundation created in 2000 by the
homonymous actor Michael J. Fox, who suffers from this disease, in
collaboration with researchers, funders, governments, medical centres in the
United States, Europe, Israel and Australia. 33 clinical centers in 11
countries take part in this initiative which collects data and biological
samples of 1,400 participants, constituting the most robust Parkinson’s
database and sample bank ever created. This comprehensive and standardized
repository of Parkinson’s disease data is a biological sample repository and
is coupled with a simple online system that allows researchers to access study
data and samples for complementary disease research in their laboratories
across the country. world. Through the platform provided by the PPMI, it is
possible to access a lot of information and data, some of a structural-
descriptive nature that is used to understand the structure of the dataset and
the semantics of the attributes of the individual tables and others containing
information regarding specific medical examinations: from the detected values
from a simple blood sample to the responses of neuro-cognitive questionnaires
up to the results of diagnostic tests, all collected in the form of tables and
placed in specific reference areas that can be classified according to the
type of study conducted on the patient. The tables contain patient data that
can be classified into seven macro-areas according to the type of information
collected:

  1. 1.

_Biospecimen:_ collection of data relating to clinical tests such as blood
sampling, DNA, lumbar puncture;

  2. 2.

_Imaging:_ I tell about relevant data through the use of imaging techniques,
such as Magnetic Resonance and DatScan;

  3. 3.

_Medical History:_ clinical history of patients from the first symptoms of the
disease to the latest health conditions. The collection includes any side
effects of medications taken, neurological, and physical test results, and so
on.

  4. 4.

_Motor MDS – UPDRS:_ collection of data relating to motor disorders using the
MDS-UPDRS scale to assess the stage of Parkinson’s disease.

  5. 5.

_Non-Motor Assessments:_ area that collects data relating to cognitive and
emotional behavioural disorders.

  6. 6.

_Study Enrollment:_ area includes the conclusive data of particular studies
conducted on patients.

  7. 7.

_Found:_ area that includes various questionnaires regarding patients’ habits
and lifestyles.

In this study, the data present in the ”Imaging” macro-area will be used to
acquire the medical images necessary for this research and the data present
within the ”Motor MDS-UPDRS” macro-area to assign a score to each image of the
dataset.

The MDS-UPDRS scale consists of four parts:

  1. 1.

_Part 1:_ Non-motor aspects of daily life. It is divided into two parts:

     * •

_Section 1A_ deals with some behavioural aspects, assessed by the examiner
based on the relevant

     * •

_Section 1B_ is completed by the patient with or without the help of the
caregiver and independently of the researcher. The questions posed to the
patient concern: sleep disturbances, urinary or constipation problems, apathy,
hallucinations and psychosis.

  2. 2.

_Part 2:_ Motor aspects of daily life. Like Part 1 Section 1B, this
questionnaire is completed by the patient with or without the help of the
caregiver but can be reviewed by the researcher to verify its completeness and
clarity. The questions include: writing, speaking, salivation, tremor, chewing
and motor block;

  3. 3.

_Part 3:_ Motor Exam. This part deals with the evaluation of the motor signs
of Parkinson’s present in the patient. The examiner shows the patient the task
that (s)he must complete and in this phase, the examiner ”evaluates what he
sees”. This analysis includes leg agility, postural stability, rigidity,
quality of movement in the act of getting up from the chair, repeated
movements of the fingers and kinetic tremor of the hand;

  4. 4.

_Part 4:_ Motor Complications. This part includes both instructions for the
examiner and instructions to read to the patient. It is filled in by the
examiner who is responsible for integrating the information obtained from the
patient with his evaluation and observation. The examiner uses both anamnestic
and objective information to evaluate two motor complications, dyskinesias
(random involuntary movements) and motor fluctuations.

The questionnaires are filled in each visit and a score is assigned which is a
numerical index indicating the progress of the disease. The higher this value
is, the more advanced the disease is. The visits are classified and ordered in
the following way:

  * •

_SC:_ Screening Visit, a preliminary visit lasting approximately eight hours,
aiming at assessing whether the patient may participate in the project;

  * •

_BL:_ Baseline Visit, the visit in which the patient’s condition was first
assessed (before he/she had received any treatment);

  * •

_V01-V15:_ sequence of tests that have been programmed for all patients;

  * •

_ST:_ Symptomatic Therapy, treatment aimed at alleviating the symptoms of the
disease;

  * •

_LOG:_ visit aimed at recording adverse events.

The data adopted in this study were extracted from the table related to two
macro-areas: Imaging, containing the medical images (Magnetic Resonance and
DatScan), and an index describing the progression of PD, calculated according
to the values of the H&Y scale. This scale describes five stages in the
progression of Parkinson’s disease. Like in [13] we also adopt the H&Y Scale
(mentioned in Hoehn and Yahr Scale) for labelling the RMI images for
supervised learning tasks.

###  0.2 Dataset creation

Before introducing the dataset it is important to point out that there exist
two types of MRI: T1-weighted and T2-weighted images. In MRIs, the
magnetically sensitive hydrogen nuclei align with the fields created by the
MRI. The radio waves released by the scanner are then absorbed as energy by
the nuclei. When the radio waves stop, the nuclei return to their initial
state, emitting tiny energy signals that the computer uses to create visual
images. This signal has two components:

  1. 1.

Longitudinal

  2. 2.

Transversal

Initially, the longitudinal signal is weak because most nuclei are tilted to
the axis. However, this signal grows as the nuclei realign. The time constant
that determines the realignment speed is denoted T1. Conversely, the
transverse signal is strong because most nuclei are in phase coherence. The
signal decays as the nuclei go out of phase as they realign. This decay is
indicated by the time constant T2[9]. The differences in T1 and T2 relaxation
times control the contrast of the MRI and depending on the case their values
are modelled to have more or less contrasted images.

We focus on a single type of MRI and we selected T2-weighted images because
the contrast and brightness are predominantly determined by the T2 properties
of tissue [28].

Images of patients were selected with the following characteristics:

  1. 1.

The patient must have Parkinson’s disease;

  2. 2.

There must be at least two MRI scans acquired in two different visits;

  3. 3.

MRIs must be T2w.

The first two constraints are dictated by the formulation of the problem, as
we want to recognize the progress of PD in a series of images.

Following these specifications, 232 MRI scans were collected related to the
brains of 63 patients with Parkinson’s disease.

For classifying the level of the patient progression [75], in our studio, five
classes were identified according to the H&Y scale. Then, noting that the
fourth and fifth classes in the PPMI dataset had few samples, the patients of
these two classes were merged into a single class.

The classes correspond to the following symptoms:

  * •

_Class 1:_ No signs of disease

  * •

_Class 2:_ Unilateral disease

  * •

_Class 3:_ Bilateral disease, without impairment of balance and mild to
moderate bilateral disease, some postural instability, physically independent

  * •

_Class 4:_ Severe disability, still able to walk or stand unassisted and
wheelchair-bound or bedridden unless aided or needing a wheelchair or
bedridden unless assisted.

The selected images are grouped into classes according to this scale. The
cardinality of the classes is reported in Table 1.

Table 1: MRI classes

ID Class | Number of elements
---|---
1 | 43
2 | 124
3 | 42
4 | 22

## Methodology

Here we describe the proposed method for designing a classifier of the level
of severity of PD using MRI images extracted from the PPMI dataset. An
overview of the proposed method is depicted in Fig. 3. It is articulated in
three phases: data preprocessing, data augmentation, and classification, as
detailed in the following.

Figure 3: Overview of the proposed method.

### Data Preprocessing

The correction and cleaning of the MRIs is an essential process, as this type
of image could present different problems, mainly due to the acquisition
method. All the pre-processing techniques were performed by using Nipype [27].
This Python project offers a standard interface to current neuroimaging
programs and makes it easier for these programs to interact with one another
inside a single workflow. The MRIs in the PPMI dataset are three-dimensional:
this means that their acquisition process is slightly different from the usual
one. Generally, an MRI consists of an image in two dimensions. From it, it is
possible to build an image in three dimensions. The acquisition takes a set of
images that cover the entire surface useful to reconstruct a three-dimensional
image. This type of image complicates the analysis, as the patient could move
during the acquisition, creating a misalignment between the various ”slices”
acquired [37].

#### Conversion from DICOM to NIfTI

The PPMI dataset contains MRI images of patients in DICOM (Digital Imaging and
Communications in Medicine) format files [50], which represents a two-
dimensional image of a single angle of the person; therefore, the first step
of the pre-processing was the reconstruction in a three-dimensional format, or
in NIfTI (Neuroimaging Informatics Technology Initiative) [74], a format
notoriously used in the medical field. Fig 4(a) shows the original DICOM
format of an MRI belonging to PPMI visualized by using the tool “MicroDicom”
[36], while Fig. 5(b) shows the three-dimensional NIfTI format if the same
image produced by the “MRIcroGL”[70] tool.

Figure 4: MircroDicom interface Figure 5: MRIcroGL interface

#### Image Registration

The image registration process consists of combining two images by
superimposing one image to the other [47]. In the medical field, image
registration allows you to combine data from multiple modalities to obtain
comprehensive patient information: it can help monitor tumour growth,
facilitate treatment verification, improve interventions, or compare patient
data with a template. To apply image registration we need a starting image, a
registration algorithm, and a target image, in our case a template. A template
is an MRI in a particular spatial location (see Fig. 6 as an example). Model
building is an iterative process that involves normalizing, aligning, and
averaging an MRI set from different patients [23]. Templates serve as a common
reference space and allow researchers to combine and compare data from
multiple people and play an important role in a variety of neuroimaging
activities:

  * •

For the spatial normalization of the input image;

  * •

For tissue segmentation;

  * •

For the analysis of the regions of interest.

Through the FLIRT 222https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FLIRT tool, image
registration was applied using the templates available on TemplateFlow [44],
as shown in Fig. 7.

Figure 6: Example of templates. Figure 7: Image registration.

#### Brain Extraction

In an MRI image, in addition to sections of the brain, there are also non-
brain tissues, such as fat, skull or neck, which can cause problems with the
analysis [38]. Brain extraction, also known as ”skull-stripping”, describes
the process of removing the skull and non-brain tissues from brain MRI scans.
it is a method for segmenting the brain from non-brain into structural and
functional data. [53]. It is also a necessary processing step in most studies
for compliance with privacy regulations. For these reasons, several brain
extraction algorithms (BEAs) are available in neuroradiological research [35].

First, a mask is generated to identify brain tissues that include the grey
matter, white matter, and cerebrospinal fluid of the cerebral cortex and
subcortical structures, such as the brain stem and cerebellum. The scalp, hard
matter, fat, skin, muscles, eyes, and bones are classified as non-brain
tissues [60].

Brain extraction was applied by using the BET tool [73]. An example of the
result of brain extraction is shown in Figs. 8 9.

Figure 8: MRI before Brain Extraction. Figure 9: MRI after Brain Extraction

### Data Augmentation

Data augmentation is a technique for creating new data from current ones to
artificially increase the amount of data [15]. It is widely used in many
machine learning tasks, such as object detection, classification, and
understanding of natural language. The creation of new and varied instances of
training data may improve the functionality and results of deep learning
models. Data augmentation techniques may include making small changes to the
data or using machine learning models to produce new data from the original
ones.

Two types of data can be distinguished, namely:

  * •

_Synthetic:_ The data are created artificially, usually through generic
networks without using elements of the real environment, but often data that
do not comply are generated [11].

  * •

_Generated:_ These are data produced by geometric alterations (such as
overturning, translation, rotation, or addition of noise) starting from
original data [2] [30].

The dataset at our disposal consisting of 232 MRIs was completely unbalanced
in the number of elements per class (see Table 1). However, the lack of MRI is
a common problem in many studies that use artificial intelligence.

At first, simple transformations such as rotation or flip were applied to
images to obtain four balanced classes, but, even applying this type of
transformation we did not obtain sufficient data for the training.
Furthermore, this technique may have some problems: applying the same
distortions to the entire MRI sequence of the same patient would maintain the
relationship of the input data but in some cases, we could create unrealistic
elements as in the case of rotations. F

One solution could have been to use generative networks (GAN) [61]. However,
we were not able to verify that the images maintained the same progression
characteristics of Parkinson’s disease among the various magnetic resonances
of an individual.

A study [51] has addressed this problem using image registration, showing that
the procedure offers a statistically significant increase in the performance
of the model trained with the generated data. To obtain realistic MRIs we
adopted this methodology, already used in the image registration phase
(subsection Image Registration), which applies transformations to the input
images by superimposing them to templates. We applied this technique using
different templates to create new elements starting from the current MRI.

We performed the registration related to data augmentation by using the
Jupiter notebook offered by Google Colab. In particular, we adopted the
paralyzing feature of the Nipype interface (in Google Colab a dual-core
machine is assigned), with the average time for processing each MRI pair in
about 400 seconds.

The proposed techniques enabled us to create new elements, useful to properly
train the neural network. In particular, we adopted six templates. The number
of images generated for each class depended on the need for balancing that
class concerning the others. For example, for class four, all six templates
were used to generate enough images to balance the number of elements in class
two. Instead, for class one two templates were used to match the number of
elements in class two. After creating the new images and balancing the
classes, other elements were generated starting from the current ones using
the flip transformation. As a result of these processes, the final dataset
distribution in classes is shown in Table 2.

Table 2: MRI classes after Data Augmentation

ID Class | Number of elements
---|---
1 | 375
2 | 375
3 | 375
4 | 375

#### Augmentated dataset’s validation

To validate the generated MRI images, a classifier trained on the MRI images
of the dataset was used. The data necessary for validation were always
collected through the resources provided by the PPMI. In total, 300 MRIs were
collected, 150 MRIs from patients with Parkinson’s disease and 150 MRIs from
volunteers without the disease. Before being used for training the network,
the images were resized and another 600 MRIs were generated from them by
applying the inversion transformation (both left and right)[64, 3]. Finally,
we used a neural network inspired by a study in which several convolutional
networks were evaluated for MRI analysis of tuberculosis patients[4], in
particular, a convolutional network in three dimensions (CNN-3D)0.1. The
dataset was divided into a training set and a validation set with a ratio of
70% and 30%. The training was carried out using the GPU provided by Google
Colab. The neural network during the training achieved good results with an
accuracy on the training set of 0.97% and the validation set of 0.91%.
Finally, to test our network on the test set, about a hundred samples were
taken from the augmented dataset, obtaining approximately 95% accuracy.

### Classification

The goal of this work is to classify Parkinson’s progression level using MRI
images. To this aim, we trained the Deep Learning classifier with ordered
sequences of MRIs of the same patient, labelled by the class of each image. We
decided to use mixed neural networks, in particular, a 3D-CNN0.1 having as the
last layers of an RNNRecurrent Neural Network.

In fact, in our case, we cannot send an MRI sequence directly to an RNN for
classification but, first, we need to extract the characteristics of the
images and then use the latter for temporal analysis of the data [20]. For the
extraction of characteristics, it is possible to use a CNN, which receives an
image and applies the convolution to find its characteristics to classify it.
By joining these two networks, we will have a network that will be able to
extract the characteristics of the images and analyze their temporal
behaviour.

Because we provide input for several MRIs to the first level, we need to avoid
merging them. Therefore, for each image taken in input, we will apply the
convolution and then we will continue the classification process with other
layers that will be in common with all the images.

Table 3: CNN-3D Layers of 3DCNN+LSTM/GRU architecture.

Layer (type) | Output Shape | Param
---|---|---
InputLayer | [(None, 128, 128, 64, 1)] | 0
Conv3D | (None, 126, 126, 62, 64) | 1792
MaxPooling3D | (None, 63, 63, 31, 64) | 0
BatchNormalization | (None, 63, 63, 31, 64) | 256
Conv3D | (None, 61, 61, 29, 64) | 110656
MaxPooling3D | (None, 30, 30, 14, 64) | 0
BatchNormalization | (None, 30, 30, 14, 64) | 256
Conv3D | (None, 28, 28, 12, 128) | 221312
MaxPooling3D | (None, 14, 14, 6, 128) | 0
BatchNormalization | (None, 14, 14, 6, 128) | 512
Conv3D | (None, 12, 12, 4, 256) | 884992
MaxPooling3D | (None, 6, 6, 2, 256) | 0
BatchNormalization | (None, 6, 6, 2, 256) | 1024
GlobalMaxPooling3D | (None, 256) | 0

Figure 10: Neural network architecture without image merging.

In particular, we adopt a network shown in Fig. 10 that takes inspiration from
3DCNN + LSTM, widely used in many similar contexts, such as for the
recognition of human gestures [86] [29] and to analyze the quality of a video
[83]. Summarizing, we can divide the network into three parts:

  1. 1.

In the first part we will find a 3DCNN that will be used to extract the
characteristics of the three-dimensional MRI and will be contained in a Time
Distributed layer that will allow us to implement the architecture described
above. Details are shown in Table 3.

  2. 2.

The second part of the network contains the layers to analyze the temporal
information, the Time Distributed
layer333https://keras.io/api/layers/recurrent_layers/time_distributed/, which
is a wrapper to apply a layer to every temporal slice of an input.

  3. 3.

The last part aims to provide the classification results.

The 3DCNN+LSTM/GRU network has been trained and evaluated by dividing the
dataset into a training set, and test set, respectively composed of 80% and
20% of the elements. We adopted k-fold validation, a resampling method that
uses different portions of the data to test and train a model on different
iterations. It is mainly used in contexts where the goal is to estimate how
accurately a predictive model will work in practice[77]. The dataset is
partitioned into k sets of equal size. Of the k sets, one is kept for testing
the model and the rest are used as training data. The process is repeated k
times, with each k set used exactly once for validation. Finally, their
results are averaged to produce a single estimate [58].

We adopted k-fold validation with k=10.

The 3DCNN architecture is reported in Table 10. The last level produces a one-
dimensional output to be used by the recurrent layers in the second part. We
adopted a GlobalMaxPool3D layer for reducing the dimension getting the maximum
values from the last convolution. To this basic model of 3DCNN other
characteristic layers of each network examined have been added, thus obtaining
six different mixed neural networks. The added layers refer to:

  * •

_Gated Recurrent Unit (GRU)_ , shown in Table 4.

Table 4: GRU layer Parameters

Layer (type) | Output Shape | Param
---|---|---
GRU | [(None, 128)] | 148224
Dense | [(None, 1024)] | 132096
Dropout | [(None, 1024)] | 0
Dense | [(None, 512)] | 524800
Dropout | [(None, 512)] | 0
Dense | [(None, 128)] | 65664
Dropout | [(None, 128)] | 0
Dense | [(None, 64)] | 8256
Dense | [(None, 4)] | 260

  * •

_sGRU_ , composed by two stacked sGRU layers, as shown in Table 5.

Table 5: sGRu layers Parameters

Layer (type) | Output Shape | Param
---|---|---
GRU | [(None, None, 128)] | 148224
GRU | [(None, 128)] | 99072
Dense | [(None, 1024)] | 132096
Dropout | [(None, 1024)] | 0
Dense | [(None, 512)] | 524800
Dropout | [(None, 512)] | 0
Dense | [(None, 128)] | 65664
Dropout | [(None, 128)] | 0
Dense | [(None, 64)] | 8256
Dense | [(None, 4)] | 260

  * •

_sbiGRU_ , composed of two bi-directional stacked GRU layers, as shown in
Table 6.

Table 6: sbiGRu layers Parameters

Layer (type) | Output Shape | Param
---|---|---
GRU | [(None, None, 256)] | 296448
GRU | [(None, 256)] | 296448
Dense | [(None, 1024)] | 263168
Dropout | [(None, 1024)] | 0
Dense | [(None, 512)] | 524800
Dropout | [(None, 512)] | 0
Dense | [(None, 128)] | 65664
Dropout | [(None, 128)] | 0
Dense | [(None, 64)] | 8256
Dense | [(None, 4)] | 260

  * •

_LSTM_ , shown in Table 7.

Table 7: LSTM layer Parameters

Layer (type) | Output Shape | Param
---|---|---
LSTM | [(None, 128)] | 197120
Dense | [(None, 1024)] | 132096
Dropout | [(None, 1024)] | 0
Dense | [(None, 512)] | 524800
Dropout | [(None, 512)] | 0
Dense | [(None, 128)] | 65664
Dropout | [(None, 128)] | 0
Dense | [(None, 64)] | 8256
Dense | [(None, 4)] | 260

  * •

_sLSTM_ , composed of two stacked LSTM layers shown in Table 8.

Table 8: sLSTM layers Parameters

Layer (type) | Output Shape | Param
---|---|---
LSTM | [(None, None, 128)] | 197120
LSTM | [(None, 128)] | 131584
Dense | [(None, 1024)] | 132096
Dropout | [(None, 1024)] | 0
Dense | [(None, 512)] | 524800
Dropout | [(None, 512)] | 0
Dense | [(None, 128)] | 65664
Dropout | [(None, 128)] | 0
Dense | [(None, 64)] | 8256
Dense | [(None, 4)] | 260

  * •

_sbiLSTM_ : it consists in two LSTM layers stacked bidirectionally shown in
Table 9.

Table 9: sbiLSTM layers Parameters

Layer (type) | Output Shape | Param
---|---|---
LSTM | [(None, None, 256)] | 394240
LSTM | [(None, 256)] | 394240
Dense | [(None, 1024)] | 263168
Dropout | [(None, 1024)] | 0
Dense | [(None, 512)] | 524800
Dropout | [(None, 512)] | 0
Dense | [(None, 128)] | 65664
Dropout | [(None, 128)] | 0
Dense | [(None, 64)] | 8256
Dense | [(None, 4)] | 260

#### Performance metrics

We aimed to compare the six considered Deep learning models to evaluate which
configuration had the best classification performance in Parkinson’s
progression classes.

The ML performance metrics for classification tasks are generally based on the
confusion matrix, an $NxN$ matrix used to evaluate the performance of a
classification model, where N is the number of target classes. The matrix
compares the actual values with those produced by the model, offering a
holistic view of the performance of the classification model and the type of
errors it is making. From the confusion matrices, it is possible to obtain the
following values:

  * •

True Positives (TP): number of samples that the model correctly predicts in
the positive class.

  * •

True Negatives (TN): number of samples that the model correctly predicts in
the negative class.

  * •

False Positives (FP): number of samples that the model incorrectly predicts in
the positive class.

  * •

False Negatives (FN): number of samples that the model incorrectly predicts in
the negative class.

To assess the performance of the selected networks the multiclass confusion
matrices are computed on the test set for each model. Fig. 11 shows the
multiclass confusion matrix computed by running the considered models. For
assessing the performance of the model the following metrics [6] based on the
multiclass confusion matrix were computed.

  * •

_Macro Averaged Accuracy._ It is computed as the average of each accuracy per
class. First, we calculate the accuracy for each class $k$ separately:

$Accuracy_{k}=\frac{TP_{k}}{TP_{k}+TN_{k}}$ (1)

Then we compute the average of all accuracy values:

$MAAccuracy=\frac{\sum_{k=1}^{K}accuracy_{k}}{K}$ (2)

  * •

_Macro Averaged Precision._ It is computed as follows. First, we calculate the
precision for each class $k$ separately:

$Precision_{k}=\frac{TP_{k}}{TP_{k}+FP_{k}}$ (3)

Then we compute the average of all precision values:

$MAPrecision=\frac{\sum_{k=1}^{K}Precision_{k}}{K}$ (4)

  * •

_Macro Averaged Recall._ It is computed as follows. First, we calculate the
Recall for each class $k$ separately:

$Recall_{k}=\frac{TP_{k}}{TP_{k}+FN_{k}}$ (5)

Then we compute the average of all recall values:

$MARecall=\frac{\sum_{k=1}^{K}Recall_{k}}{K}$ (6)

  * •

_Macro Averaged F1._ MAF1 is the weighted average (harmonic average) of
precision and recall, computed as follows:

$2*\left(\frac{MacroAveragePrecision*MacroAverageRecall}{MacroAveragePrecision^{-1}+MacroAverageRecall^{-1}}\right)$
(7)

  * •

_Macro Averaged OVR AUC._ It quantifies the model’s ability to distinguish
each class (One Versus Rest). To derive this metric we need to compute the AUC
of each ROC curve for each class [43] and to compute the average.

Table 10: Model performance comparison.

Model | MAAccuracy Validation | MAAccuracy Test | MAPrecision | MARecall | MAF1
---|---|---|---|---|---
3DCNN+GRU | 82% | 86,6% | 87,2% | 86,6% | 86,4%
3DCNN+sGRU | 78% | 86,2% | 86,4% | 86,2% | 86,2%
3DCNN+sbiGRU | 86% | 86,6% | 86,5% | 86,6% | 86,5%
3DCNN+LSTM | 86% | 87,9% | 87,9% | 87,9% | 87,7%
3DCNN+sLSTM | 70% | 77% | 77,5% | 77% | 76,7%
3DCNN+sbiLSTM | 81% | 84,5% | 84,4% | 84,5% | 84.4%

Table 11: Results Macro Averaged OVR AUC.

Experiment | 0 | 1 | 2 | 3 | MA OVR AUC
---|---|---|---|---|---
3DCNN+GRU | 99,1% | 82,7% | 89,1% | 93,3% | 91.05%
3DCNN+sGRU | 98,8% | 88% | 86,3% | 90% | 90.76%
3DCNN+sbiGRU | 98,3% | 89,7% | 85% | 91,3% | 91.08%
3DCNN+LSTM | 99,4% | 87,5% | 86,3% | 94,4% | 91.90%
3DCNN+sLSTM | 97,5% | 86,3% | 79,1% | 75,50% | 84.60%
3DCNN+sbiLSTM | 97,7% | 86,6% | 82,5% | 91,90% | 89.68%

##  1 Models’ assessment

Results of the performance of the six models on the test set are detailed in
the following and summarized in Tables 10 and 11. All the models have been
trained for 35 epochs.

As shown in Table 10 on the test set the 3DCNN + LSTM network performs better
than the others, even if the results are close, except for 3DCNN+sLSTM which
has the lowest performance. From the AUC values for each class (Tab. 11), it
can be seen that all the models can recognize class 0 very well, compared to
class 2 where there is more variance between the various results. The best
Macro Averaged OVR AUC result, 91.9%, is still reached by 3DCNN+LSTM, whose
performance varies from 86.3% in the case of the class 2 to 99.4% for the
class 0.

Figure 11: Confusion matrix.

## Conclusion and Future Work

Our research centred on developing a deep neural network-based novel model to
detect and assess Parkinson’s disease progression. We created and trained a
model that combines a recurrent layer (LSTM or GRU) with a 3D convolutional
neural network (3DCNN) using analysis of the Parkinson’s Progression Markers
Initiative dataset.

The outcomes show how good our method is; specifically, on four classes of
Parkinson’s disease development, the 3DCNN + LSTM model acquired a Macro
Averaged OVR AUC of 91.9%. This performance outperforms other tested models,
highlighting the validity of our architecture in accurately classifying
different stages of progression.

The precision of the model was enhanced by careful consideration given to data
preprocessing, which included removing non-brain content and fixing
acquisition mistakes. To maintain consistency in the results throughout the
various advancement classes, the dataset was balanced by the application of
template-model-based data augmentation approaches.

Nonetheless, we acknowledge that our research has certain limitations, such as
reliance on the dataset’s data and the requirement for additional model
validation using a wide range of clinical data. To increase model accuracy,
future advancements might investigate other deep learning strategies and use
multimodal data.

In summary, our study constitutes a major advancement in the field of
Parkinson’s disease diagnosis and assessment. Adopting cutting-edge techniques
based on deep neural networks provides a strong foundation for future
advancements and therapeutic applications while opening up new avenues for
understanding and controlling the illness

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