Literature Review chap:guide

This chapter provides a comprehensive review of the current advancements in
glaucoma progression detection. We begin with a meta-review of survey papers
and an examination of some landmark studies. This is followed by an
introduction to clinical, traditional, and machine-learning methods to detect
disease progression, which covers the structural and functional aspects of
evaluation techniques. Through this, we aim to learn the latest advancements
and the challenges and pitfalls in tracking glaucoma. We then transition to
the complex structure-function relationship and emphasize how it has improved
the performance of various traditional and machine-learning detection methods.
We will conclude the chapter by presenting an in-depth review of the current
state of the art in deep learning approaches for detecting progression, which
will form the basis of our research.

Background

Glaucoma, specifically glaucoma progression, has been a significant area of
research in Ophthalmology. Recent advancements in artificial intelligence (AI)
algorithms and the availability of large medical data sets have shown
potential and a growing interest in its application for glaucoma progression
detection and management. Numerous studies have tried to explore the
effectiveness of AI in improving the diagnosis of glaucoma progression over
standard clinical practice and traditional methods. These studies have relied
on regular follow-up assessments of several clinical parameters such as
demographics (age, sex), electronic health records (EHR; IOP, central corneal
thickness - CCT, cup-disc ratio - CDR) data, structural properties (ONH
change, RNFL thickness) and functional characteristics (VF sensitivity) to
identify glaucomatous change. While these features have improved the
predictive accuracy, it still needs to be determined which set of data offers
the best representation of glaucoma progression, balancing data acquisition,
ease of access, and minimal expertise interpretation.

Modeling glaucoma progression has been challenging since combining spatial and
temporal aspects with a single technique, even with AI, is difficult.
Confounding risk factors, test-retest variability, and secondary diseases also
make it hard to categorize glaucoma progression. Another contention amongst
all studies is the inconsistent definition of progression and how it’s
measured. Although established methods exist, such as event-based GPA or
trend-based point-wise linear regression (PLR), there are no universal
criteria to define progression, which makes it difficult to develop AI
algorithms for detection. Nonetheless, clinicians have overcome many
challenges applying AI, especially deep learning techniques, to predict
glaucoma progression using imaging and health records. We provide a brief
review of such studies, discussing both the advantages and challenges of AI in
glaucoma research shedding light on how AI overcomes limitations.

Review

Several surveys have reviewed primary studies focusing on methods used to
detect glaucoma progression using either structure, function, or both. These
papers present various perspectives on applying AI and deep learning in
tracking glaucoma progression. For instance, an article by thompson2020review
lays a solid foundation, exploring how advances in computing technology and
the availability of large datasets have enhanced glaucoma diagnosis. This
paper reviewed 91 studies on traditional and machine learning methods,
illustrating how the techniques can be integrated into clinical practice. The
survey found that while evaluations have become more accurate with imaging
data, there remains a drawback in the inconsistent definitions of the
reference standard. This underscores the importance of rigorous validations
alongside expert opinions before AI techniques are deemed suitable for
screening. Although the primary focus of this article is on glaucoma
diagnosis, studies on tracking its progression have drawn similar conclusions.
These techniques have yet to be incorporated into clinical decision-support
systems and require thorough research before implementation.

A review by mirzania2021applications on AI for glaucoma detection has
emphasized the influence of data quality for improving the DL algorithm’s
performance. They imply that datasets with better demographic, clinical,
structural, and functional representations obtain better performance. In a
more specific case, asaoka2023prediction have reviewed 108 papers, and
hu2020functional have reviewed 207 papers on the prediction of functional
progression of glaucoma using visual fields. Both these surveys highlight that
although VF is highly effective in predicting progression, the importance of
model adjustment to specific nuances of experiment design (e.g., irregular
testing) before applying AI is paramount. Both papers have outlined that none
of the techniques can yet be incorporated in clinical settings due to a lack
of validation with a universal ground truth. Similarly, a paper reviewing 30
papers by bussel2014oct demonstrating the importance of structural parameters
like RNFL from OCT scans in detecting disease progression also fails to
identify a common reference standard. Even though RNFL is a precise measure,
providing AI methods with accurate estimates for structural progression, it
faced challenges like limited reliable datasets, outliers, and the absence of
long follow-up data. On the upside, these papers have prompted the use of
combined structure and function index for improved glaucoma progression.

A more holistic view of DL approaches to detect and monitor glaucoma
progression is provided in the works of mayro2020impact, 71 papers and
thakur2023use, 108 papers. Both articles offer distinct but overlapping
perspectives of DL methods using structure, function, and clinical data,
highlighting innovative techniques for progression detection. The first paper
demonstrates the transformative potential of DL methods to generate new image
data, thereby forecasting disease trajectory. Various methods discussed in
this paper underscore the utility of DL algorithms in efficiently processing
information from multiple modalities, providing accurate predictions. It
succeeds in pinpointing several limitations regarding data-centric modeling
techniques, critiquing the algorithm’s inability to effectively analyze
confounding factors, sensitivity to disease manifestations, and image quality.
The second paper, on the other hand, adopts a broader approach to analysis,
covering various ML and DL methods with multiple modalities (clinical,
structure, function) for disease progression. It differs from the first in its
ability to highlight variability in results, inconsistencies in datasets, lack
of standard progression definitions, and the ability to provide a nuanced view
of methods. This paper sets its narrative review specifically for glaucoma
progression detection and provides evidence for the translational potential of
current research in the field. Although all these papers offer critical
reviews of the evolving AI field, laying down the groundwork for research, a
more granular analysis that would help clinical understanding and provide a
computational and data-centric perspective is necessary.

Landmark Glaucoma Studies

Several foundational glaucoma studies done over the years have highlighted the
importance of early detection and intervention in glaucoma progression. Table
tab:studies summarizes some landmark studies demonstrating the importance of
timely intervention. The seminal work in The Early Manifest Glaucoma Trial
(EMGT) showed that individuals are at a higher risk of progression if timely
treatment is not provided (leske1999early). It is one of the first studies
that identified age as a significant risk factor in VF deterioration. The
Advanced Glaucoma Intervention Study (AGIS) emphasized the persistent risk of
visual field loss throughout the follow-up, even after intervention (agis).
The Ocular Hypertension Treatment Study (OHTS) proved that the risk of eyes
evolving into POAG extended over 15 years, making glaucoma one of the most
prolonged chronic progressive diseases (ohts). Both AGIS and the OHTS showed
that lowering IOP can substantially delay the onset of POAG, underscoring the
importance of preemptive care.

Further studies, like the Collaborative Initial Glaucoma Treatment Study
(CIGTS), compared the efficacy of surgical and medical interventions in IOP
management and their implications for vision-related quality of life (cigts).
On a tangential yet similar note, the Collaborative Normal-Tension Glaucoma
Study (CNTGS) illustrated that IOP reduction is paramount for therapeutic
impact on disease progression. The European Glaucoma Preventing Study (EGPS)
supported the findings of other studies by recognizing the influence of IOP as
a risk factor (egps). The Glaucoma Laser Trial (GLT) by glts and the United
Kingdom Glaucoma Treatment Study (UKGTS) by ukgts affirmed the above studies
in determining the efficacy of IOP management through medical interventions.
It showed that IOP management is an effective way to reduce the rate of visual
field progression during the initial POAG stages. Collectively, these
pioneering studies elucidated the relationship between structural nuances such
as IOP fluctuations and their effects on functional components such as the
visual field deterioration, thereby providing a holistic understanding of
components in glaucoma progression. These studies also showed that early
glaucoma progression detection and monitoring are pivotal for efficient and
targeted therapeutic interventions.

@ *9R ¿R @ A brief review of some landmark glaucoma studies
[bl]Name & [bl]Study
Type [bl]Citation [bl]Design [bl]Dataset [bl]Follow-up
Period [bl]Treatment [bl]Outcome
Measures [bl]Findings
Early Manifest Glaucoma Trial (EMGT) [leske1999early] Treatment vs No
Treatment Randomized, Clinical Trial. 255 Patients with mean age 68.1yrs; 66%
women >4 years Laser Trabeculoplasty + Betaxolol VF loss in 3 consecutive
C30-2 Humphrey tests or Optic Disc Changes Higher progression rate in controls
group; High IOP frequently followed by progression; Old age was associated
with faster progression.
Advanced Glaucoma Intervention Study (AGIS) [agis] Treatment vs Treatment
Randomized Clinical Trial 591 Patients (789 eyes) age 35-80yrs; 54.3% Female;
56.2% AA 7 years Series of Laser Trabeculoplasty and Trabeculectomy VF tests
using 24-2 Humphrey Field Analyzer and Visual Acuity Tests Over half the eyes
showed VF progression throughout the follow-up after intervention.
Ocular Hypertension Treatment Study (OHTS) [ohts] Treatment vs No Treatment
Randomized Clinical Trial 1636 Patients age 40-80yrs; 57% Female; 25% AA >5
years Topical Ocular Hypotensive Medication Reproducible 30-2 Humphrey VF
deterioration and reproducible stereoscopic optic disc deterioration Risk of
developing POAG continues over at least 15 yrs of follow-up; High risk
individuals more likely to respond to treatment.
Collaborative Initial Glaucoma Treatment Study (CIGTS) [cigts] Treatment vs
Treatment Randomized, Controlled Clinical Trial 607 Patients age 25-75yrs; 45%
Female; 38.1% AA >5 years Topical Ocular Hypotensive Medication vs Immediate
Trabeculectomy Sustained Progression in VF loss in 24-2 HFA. Both treatments
effective in slowing down POAG; Bpth group displayed similar rates of VF loss
over time.
Collaborative Normal-Tension Glaucoma Study (CNTGS) [cntgs] Treatment vs No
Treatment Randomized, Controlled Clinical Trial. 145 Subjects age 20-90 yrs; 5
years Topical Medication or Laser Trabeculoplasty VF Progression and
Stereoscopic Optic Disc deterioration Reducing IOP in patients with Normal-
Tesion Glaucoma significantly reduced progression in VF.
European Glaucoma Preventing Study (EGPS) [egps] Treatment vs No Treatment
Randomized, Double-Blinded, Controlled Clinical Trial 1077 Subjects age $\geq
30$ years; 54.4% Female 5 years Topical Ocular medication IOP, VF Tests and
Stereoscopic Optic Discs Lower IOP associated with reduced risk of glaucoma
oset; Similar clinical characteristics of POAG group with OHTS.
Glaucoma Laser Trial (GLT) [glts] Trearment vs Treatment Randomized Clinical
Trial. 271 Patients (542 eyes) age >35 yrs; 56% Female; 43% AA >2 years (<9
years) Topical Medication vs Laser Trabeculoplasty IOP, VF Tests and Optic
Disc Changes Laser Trabeculoplasty had significant effect in lowering IOP than
eyes with topical medication.
United Kingdom Glaucoma Treatment Study (UKGTS) [ukgts] Treatment vs No
Treatment Randomized, Controlled Multicenter Treatment Trial. 516 Subjects
mean age 66 ±11 yrs; 47.1% Female 2 years Topical Ocular Medication VF 24-2
HFA II Testing, Confocal Scanning Laser Ophthalmoscopy, Scanning Laser
Plarimetry, OCT, Monoscopic Optic Disc Photograph, Fundus Photographs,
Tonometry IOP treatment significantly reduced VF progression in early POAG;
Pointwise Linear Regression helpful in detecting progression.

##  1 Glaucoma Progression Detection using Assessments of Visual Fields

###  Clinical Assessments and Event-Based Methods

Visual field tests are usually used to assess the glaucoma progression leading
to functional impairments. VF tests are obtained by measuring the patient’s
response to contrast stimuli using various perimetric data as shown in Section
LABEL:ss:_func. It is often measured as the intensity of light stimulus on a
logarithmic scale (dB). VF has complex properties; therefore, assessment of
progression requires additional analysis of the perimetric data such as
clinical gradings, glaucoma change probability scores, event-based analysis,
or trend-based analysis ([hu2020functional]). Clinical judgments are
subjective and have been found to have moderate intraobserver agreement
$(\kappa=0.45-0.55)$ ([aref2017detecting]). Landmark studies have implemented
some form of scoring system to detect progression. The most commonly used and
explicit scoring criteria is the glaucoma change probability criteria
developed by EMGT ([leske1999early]), which detected early progression. AGIS
and CIGTS have used similar methods, which have been repurposed for
identifying progression by researchers ([KATZ1999391]). Studies have shown
that all these methods have comparable statistics for detecting progression
but have various tradeoffs between specificity and sensitivity
([hu2020functional, 10.1001/archopht.117.9.1137]). The Glaucoma Change
Probability (GCP) analysis and later the GPA were inspired by the EMGT and is
now a commonly used event-based analysis. Research on the comprehensive
analysis of the GPA found that there is a strong correlation between GPA and
thorough, objective clinical criteria with 93% sensitivity and 95% specificity
([arnalich2009performance]), which has shown to be a promising method for
detecting VF loss. Some studies showed that GPA was conservative in declaring
VF sequences progressing ([roberti2022glaucoma, TANNA2012468,
doi:10.5301/ejo.5000193]). These papers showed that event-based GPA has a
moderate agreement with expert clinical judgments ([TANNA2012468]). In other
studies, the GPA method was found to be susceptible to high false positive
rates when predicting ”possible progression” in patients with high test-retest
variability ([ARTES20142023, 10.1167/tvst.7.4.20]). Future studies have tried
to modify GPA, integrate sophisticated analysis, or add structural GPA to
improve specificities ([TANNA2012468, medeiros2012integrating,
leung2010evaluation]). Ubiquitously, all papers showed GPA could detect early
signs of VF deterioration than other methods but are met with the same
intrinsic limitations as SAP tests. Authors recommend using a sufficient
number of VF tests, which can be time-consuming, require effort, or are prone
to test-retest variability.

###  Linear Regression and Trend-Based Methods

Trend-based analysis has been widely used to detect glaucoma progression
utilizing some form of linear regression on VF test parameters or indices.
They predict progression by quantifying the VF deterioration as a rate of
change over time. This not only helps detect progression but also identifies
the onset of progression, rapid progressors, time to change, etc. An eye is
classified as progressing using trend-based analysis if the rate of change is
negative (usually $<0dB/yr$) and is statistically significant (usually
$p<0.05$). Unlike GPA, which generally needs global context for analysis,
trend-based methods can be applied to global (MD), sectoral, or individual
points (pattern standard deviation - PSD or visual field index - VFI)
([hu2020functional]). Trend-based methods agreed with GPA and clinical
judgments ([casas2009visual, medeiros2023validation, nouri2004prediction]).
Specialized analysis software using linear regression methods such as
PROGRESSOR also showed similar sensitivities for detecting progression
([de2012comparison]). Adding mixed-effects, prior distribution, and clinical
priors improved predictive accuracy ([zhang2015log, nouri2004prediction,
swaminathan2022rates]). Trend analysis using different parameters of
perimetric tests such as VFI, MD, or PSD was also found to correlate well,
identifying a similar proportion of eyes progressing. Still, they have
intrinsic limitations ([cho2012progression, fitzke1996analysis]). The trend-
based analysis detected progression accurately in patients with longer follow-
ups, a common pattern observed in multiple studies ([casas2009visual]). When
comparing how spatial information helps in detecting progression, generally,
PLR or cluster trend analysis (CTA) performed better, but no consensus was
observed ([viswanathan1997early, vesti2003comparison, mayama2004statistical]).
All these methods had limitations and trade-offs between experiment design,
parameters to be used, and test results, often affected by confounding
factors. Another study by [quigley1996rate] indicated that linear regression
might not be sufficient to detect progression in the presence of bilateral
disease. Adding statistical methods into linear regression has been shown to
improve sensitivity in VF compared to linear regression and PLR alone. For
example, the ANSWERS (Weibull Error Regression) method by [zhu2014detecting]
obtains 15% less prediction error than permutation of PLR (PoPLR). Overall,
trend-based methods were moderately accurate, but more emphasis is needed on
the experiment design and how to analyze trend data.

###  Probabilistic Methods

Table 1 summarizes complex modeling strategies used in glaucoma progression
detection. Probabilistic methods provide a framework for modeling and
understanding uncertainties in data. In the context of detecting glaucoma
progression, these methods offer a robust and adaptive approach, taking into
account the inherent variability and intricacies of ocular data. Probabilistic
methods, especially those based on Bayesian frameworks, have been extensively
explored for detecting glaucoma progression using visual fields (VF).
[medeiros2012combining] showcased the Bayesian Hierarchical Model’s
effectiveness, which integrates trend and event analyses. This approach
significantly outperformed ordinary least squares (OLS) regression, with a 98%
detection rate at 96% specificity. Concurrently, studies like
[warren2016statistical] and [berchuck2019diagnosing] employed Conditional
Autoregressive (CAR) models, each showing strong agreements with expert
judgment on glaucoma progression. [betz2013spatial] further reinforced CAR’s
utility, demonstrating it outperforms standard PLR methods. In another study,
the Empirical Bayes Estimates of Best Linear Unbiased Predictors (BLUP)
introduced by [medeiros2012improved] significantly reduced mean squared error
in predicting future VF impairments compared to traditional OLS. In the same
domain, [montesano2021hierarchical] proposed a Hierarchical Bayesian Analysis
model that showed improved VF progression estimation for glaucoma progression.
Studies also demonstrated probabilistic methods can be repurposed for
classification and unsupervised learning. For example, [yousefi2014learning,
yousefi2018detection] series of studies from 2014 to 2018 utilized Gaussian
Mixture Models (GMM) and Variational Bayesian techniques for detecting
progression. These models, especially GMM Expectation Maximization with
Permutation of Points (GEM-PoP), were found to detect with high sensitivity
and outperformed several baseline and clinical methods in predicting
progression. [goldbaum2012progression] introduced a Variational Bayesian
Independent Component Mixture Model (VIM), adding valuable clinical insights.
Collectively, these probabilistic methods displayed superior performances
against established reference standards and highlighted their potential to
offer better interpretability and robustness in tracking glaucoma progression
compared to conventional methods.

###  Machine Learning Methods

ML methods have gained significant traction in detecting glaucoma progression
using visual fields (VF). [o2012visual] introduced the Permutation Analysis
PLR (PoPLR), which reordered permutations to yield a continuous estimate of
deterioration while maintaining control over specificity.
[shuldiner2021predicting] employed traditional classifiers such as Support
Vector Machines (SVM) and Fully Connected (FC) networks to predict rapid
progression using initial VF tests, achieving a notable AUC of 0.72. In
another study, [jones2019identifying] combined Principal Component Analysis
(PCA) with a Soft Voting Classifier, using both longitudinal IOP and VF data,
which enhanced accuracy in predicting rapid progression with an AUC of 0.83.
[sample2005unsupervised] applied Independent Component Analysis (ICA) in an
unsupervised setting, outperforming traditional criteria like AGIS and EMGT.
Unique methods such as Archetypal Analysis were also developed
([wang2019artificial] to identify representative progression patterns,
outperforming multiple established methods. [saeedi2021development] used
numerous ML classifiers, from Random Forests to CNNs, to distinguish
progressing from non-progressing eyes, all of which achieved high sensitivity
and specificity, outperforming conventional approaches. When compared with
probabilistic methods, ML techniques, especially when combined with
unsupervised learning or ensemble classifiers, are adept at handling the
intricacies of VF data for glaucoma progression. While probabilistic methods
usually need prior knowledge to incorporate uncertainty, machine learning can
learn the complex non-linear patterns directly from the datasets and produces
superior performance in terms of accuracy and generalizability for detecting
glaucoma progression using VF data.

###  Deep Learning Methods

DL methods have significantly advanced in detecting glaucoma progression using
VFs, particularly by leveraging the data’s complex spatial, temporal, or
spatiotemporal relationships. Several models, such as CascadeNet-5 utilized by
[wen2019forecasting] and the generalized Variational Autoencoder (VAE) by
[berchuck2019estimating], focus primarily on spatial patterns in VF,
predicting future Humphrey VF (HVF) points and trajectories of progression
with commendable accuracy. On the other hand, we saw that models that use both
spatial and temporal aspects of VF data provided some of the most promising
results. For instance, [dixit2021assessing] developed a Convolutional Long
Short-Term Memory (ConvLSTM) network, which captures spatiotemporal
relationships in VF and demonstrated that combining VF data with clinical
measures further improves predictive accuracy. [hosni2023bidirectional] use of
Bidirectional Recurrent Model (Bi-RM) and [sabharwal2023deep] implementation
of ConvLSTM techniques further emphasized the efficacy of incorporating time-
series data in defining VF progression patterns by producing state of the art
results. Unsupervised DL methods are another such technique that is shown to
capture subtle characteristics of progression. For example,
[berchuck2019estimating] developed a VAE latent space by reconstructing
longitudinal VF data to effectively predict rates and trajectories of
functional progression. The unsupervised Deep Archetypal Analysis by
[yousefi2022machine] is another method that illustrates DL-based unsupervised
methods can predict early signs of glaucoma and potential rapid progression
accurately. Compared to the previously discussed probabilistic and traditional
ML techniques, DL models, especially those integrating spatiotemporal
features, were found to be more holistic and produced a nuanced understanding
of glaucoma progression, presenting an essential tool for glaucoma progression
detection for researchers.

###  Time Series Analysis and Forecasting Methods

Time series and forecasting models emphasizing predicting future VF
deterioration have also been researched in glaucoma progression. Specifically,
models like the intrinsic CAR technique in studies by [warren2016statistical]
and [betz2013spatial] were inherently geared towards spatiotemporal data,
making it proficient at capturing the intricacies of VF changes over time.
[berchuck2019diagnosing] extended the CAR framework and integrated it with
Bayesian and Generalized Linear Mixed Models (GLMM) to improve the model’s
predictive capability, especially highlighting the improvements made by
spatiotemporal modeling over strictly spatial ones. Outside the traditional
probabilistic CAR domain, [garcia2019using] employed traditional estimation
techniques such as the Kalman Filter Estimator, a classical time-series
forecasting approach. This model accounts for measurement uncertainties and
inaccuracies, offering holistic forecasting results for disease trajectory,
mainly seen with normal tension glaucoma (NTG). A holistic comparison of all
the above methods shows that DL methods provide adaptability and potential in
handling large and complex datasets. Probabilistic schemes like CAR and Time-
series models like the Kalman Filter provide a more structured, principled
approach to predicting progression. Traditional models incorporate domain
knowledge well, ensuring outcomes align with known clinical expectations and
thus ensuring greater trustworthiness in clinical settings. DL model, on the
other hand, although it provides better accuracies, needs to be carefully
tuned to obtain desired outcomes.  @ *11R ¿R @ A Review of Methods for
Detection and Prediction of Glaucoma Progression using Visual Field Tests
[bl]Citation & [bl]Setup [bl]Algorithm/
Method [bl]Dataset [bl]Data
Type [bl]Follow-up
Period [bl]Reference
Standard [bl]Baseline
Method [bl]Model
Output [bl]Outcomes [bl]Summary
[medeiros2012integrating] Regression Bayesian Hierarchical Model integrating
trend and event 711 eyes - glaucoma and suspects; 55 eyes - stable VFI from
SAP with GPA prior 5 reliable tests, 5 years 2 consecutive VF test has PSD
<0.05 from baseline and GPA OLS regression slopes Bayesian slopes of change
for VFI 98% by Bayesian vs 50% OLS for 96% Specificity Integrated Bayesian
Model significantly better than OLS
[warren2016statistical] Regression Intrinsic Conditional Autoregressive Model
Train - 191 eyes, Test - 100 eyes 24-2 VF tests from HFA 5 follow-ups, 4.34
years Clinicians Grading and GPA Regression Models Deviance Information
Criterion,VF Progression Slopes 0.8 AUC, 92% Sensitivity, 95% Specificity
Model agrees with Progression by Experts
[o2012visual] Regression Permutation Analysis PLR 944 eyes 24-2 HFA II SAP
tests 10 exams, 8 years Slopes <0 and <-1dB/y with p<0.05 PLR, MD Linear
Regression PoPLR slopes and p value 12%, 29%, 42% hit-rate at 5th, 8th and
final exams PoPLR uses permutation reordering to get continuous estimate of
deterioration, allows for control over specificity
[medeiros2012improved] Regression Empirical Bayes Estimates of BLUP 643 eyes
VFI from SAP Tests 10 tests, 6.5 years significant negative slope (alpha =
0.05) OLS Regression Slopes of change with future VFI predicitions MSE 32.3 vs
13.9 for Bayesian vs OLS BLUP is beneficial in predicting future impairment
[betz2013spatial] Regression Conditional Autoregressive (CAR) Model 194 eyes
Full SAP threshold, SITA exams 7.5 tests, 2.5 years Clinical judgements on VF
reports PLR Slopes of change Progression: Significant Slopes of change with
varying alpha 68% sensitivity, 73% specificity Model outprforms PLR method
[berchuck2019diagnosing] Regression Bayesian CAR with GLMM 191 eyes VF SAP
tests from HFA II 7.4 tests, 2.5 years signifiant LR slope at all VF locations
PLR Method Progression: Signigficant LR slopes 0.74 AUC for SpatioTemporal
Method Spatiotemporal method better than Spatial method in predicting
progression
[montesano2021hierarchical] Regression Hierarchical Bayesian Analysis
Modeling: 146 eyes, Analysis: 3352 eyes 24-2 SAP test from SITA HFA III 10
VFs, 4 years P-score >0.5, significant negative slopes OLSLR and PoPLR
Progression Score (one sided p-value for slope) 57% Hit-rate at 95%
Specificity Bayesian Model better estimates VF progression
[bryan2017bayesian] Regression Bayesian Hierarchical Model 276 eyes 52 points
from 24-2 SAP tests from HFA 10.5 years \- \- VF Slopes, Posterior Predictiive
Checks, Deviance Information Control (DIC) DIC=1075212, slope = -0.31dB/year
for progressing Two-stage modeling is beneficial to explore progression
[wen2019forecasting] Regression CascadeNet-5 and other DL models 8263 eyes
(80% train, 10% test) 24-2 SAP tests from SITA HFA II 3.6 visits, 3.5 years
Mean and STD of rate of progression from EMGT \- future HVF points, PMAE 2.47
dB PMAE, correlation with MD: 0.92 DL can predict future spatiotemporal HVF
[garcia2019using] Regression Kalman Filter Estimator 263 eyes NTG, 601 eyes
HTG demographics, IOP, VF MD, VF PSD 5.9 years NTG, 6.3 years HTG \- Null
Model, Linear Regression future MD, PSD and IOP values Predictive accuracy:
87.2% KF-NTG, 86% KF-HTG vs 86.4% Null Model, 72.7% LR KF forecasts disease
trajectory with NTG
[dixit2021assessing] Regression and Classification Convolutional LSTM with
VFI, MD, PLR slopes 11242 eyes 54 points 24-2 VF tests, IOP, CCT, CDR 4
follow-ups statistical significant negative slopes MD Slope, VFI Slope, PLR
Slope stable and progressing 0.89-0.93 AUC with VF and Clinical, 0.79-0.82 AUC
with VF ConvLSTM captures spatiotemporal relationships accurately, VF with
clinical data more accurate than VF alone
[hosni2023bidirectional] Regression and Classification Bidirectional Recurrent
Model (Bi-RM) Train: 5413 eyes, Test: 1272 eyes 54 points 12-2 HFA using ITT 6
tests, 3 years \- Linear Regression, Term Memory progressing vs non-
progressing, VF points 92.6% AUC, 3.61 dB Prediction MAE Bi-RM is predictive
of VF progression
[hemelings2023predicting] Regression and Classification ResNet50 with OLSLR or
Huber Train: 1839, Valid: 272, Test: 271 eyes 24-2 VF HFA tests 8.5 tests,
5.37 years significance at different negative slope cutoffs \- progression:
significant negative slopes AUC: 0.67 with OLS at 0.65 with Huber DL predicts
VF progression using baseline fundus
[goldbaum2012progression] Classification Variational Bayesian Independent
Component Mixture Model (VIM) 2085 eyes 24-2 SAP test from HFA II, SITA 6.7
tests, 4 years GPA outcomes, stereophoto clinical judgements GPA outcomes
Progression: LR slopes from POP estimates 26.3% vs 14.5% accuracy for PGON
eyes wrt GPA POP adds information to clinicians to detect VF progression
[sabharwal2023deep] Classification Convolutional LSTM 8705 eyes (80% train,10%
valid, 10% test) VF SAP tests 12 VF tests, 12 years event (GPA) and trend-
based (LR) methods clinical assessment (EHR at final VF) progressing vs non-
progressing 0.78-0.94 AUC with all VF DLM defines VF worsening successfully
[shuldiner2021predicting] Classification SVM, FC Network, Random Forest 22925
eyes 24-2 VF tests 5 VF tests significant negative MD slope <-1dB/y Logistic
Regression, naive Bayes rapid and non-rapid progressors AUC: 0.72 with SVM and
FC Net MLA predicts rapid progression with initial VF tests
[saeedi2021development] Classification MLCs: Logistic Regression, RF, xgBoost,
SVC, CNN, FCN 131156 eyes (80% train, 10% test), 161 eyes clinical test 24-2
VF SAP from SITA tests 6.9 follow-ups, 6.3 years majority vote by MD slope,
VFI slope, AGIS criteria, CIGTS score, PLR, PoPLR \- progressing vs non-
progressing 0.83-0.88 sensitivity, 0.92-0.96 specificity MLC had better and
balanced predictions than conventional methods
[yousefi2014learning] Classification GEM + longitudinal slopes 76 eyes
progress, 91 eyes stable 52 points from 24-2 SITA SAP tests from HFA II
progressing: 5.5 follow-ups, 2.7 years, stable: 4.7 follow-ups, 4.2 weeks
expert grading of serial stereo photos SAP GPA, VFI LR, MD LR stable vs
progressed 28.9% Sensitivity at 95% Specificity Accuracy of GEM outperforms
baseline and clinical methods
[jones2019identifying] Classification PCA + Soft Voting Classifier 571
patients (80% train, 10% test) IOP, MD, PSD data 6 years statistical
significant negative slopes <-1dB/y \- rapid and non-rapid progressors 0.83
AUC for predicting rapid progression IOP and VF data improves accuracy for
Rapid Progression
[yousefi2018detection] Unsupervised Gaussian Mixture Model 2085 eyes -
glaucoma VF 24-2 SAP tests 5 follow-ups, 9 years PSD <0.05 in SAP tests wrt
Baseline and reproducible at least once Global, Region, Point wise Linear
Regression Time to detect progression using ML slope and p-value 3.5 years vs
3.9 years to detect progression ML outperformed other methods
[wang2019artificial] Unsupervised Archetypal Analysis method: 12217 eyes +
validation: 400 eyes 24-2 VF SAP Tests 5 follow-ups, 5 years Event-based and
trend-based clinical assessment MD Slope, AGIS score, CIGTS score, PoPLR Rate
of Archetype change ONL 51% accuracy and 77% correct rejection rate Achetype
significantly ouperformed all methods
[sample2005unsupervised] Unsupervised Independent Component Analysis (ICA) 191
eyes - glaucoma and suspect 24-2 and 30-2 VF SAP Tests 3 follow-ups, 6.24
years Clinical assessment of Stereoscopic Photos + PGON AGIS and EMGT scoring
Progressed vs Non-progressed 16.7% progressing, 31% PGON vB-ICA outperforms
AGIS and EMGT criteria
[berchuck2019estimating] Unsupervised generalized Variational Autoencoder 3832
eyes (80% train, 10% valid, 10% test) 24-2 SAP VF Test 7.61 visits, 4.95 years
significant negative rate of change (alpha = 0.05) PW Linear Regression and
OLSLR slopes with MD 24-2 SAP VF points, rates of change using latent
dimension MAE: VAE 5.14dB vs PW 8.07dB VAE can predict rates ad trajectories
of progression
[yousefi2022machine] Unsupervised Unsupervised Deep Archetypal Analysis 205
eyes from OHTS 30-2 VF from SITA HFA 16 years OHTS expert-identified patterns
\- MD of 18 VF clusters, GEE LR slopes -2.7 dB MD at glaucoma, -5.5dB MD at
last visit Automated ML system predicts early sign and rapid progression
[yousefi2016unsupervised] Unsupervised GEM Progression of Patterns and VIM
2143 eyes 52 points in 24-2 SITA SAP tests progress: 14 follow-ups, 9.1 years
expert grading of serial stereo photos signifiacnt PoPLR, MD LR, VFI LR slopes
stable vs progressed AUC: 0.86 for GEM-PoP, 0.82 VIM-Pop GEM-PoP was
significantly more sensitive to PGON

##  2 Detection of Structural Progression

Several methods have been developed for detecting structural progression in
glaucoma by objectively and quantitatively measuring the anatomical changes in
the eye, particularly the ONH and the RNFL. Comparison of advanced imaging
techniques for glaucoma progression, such as OCT, scanning laser polarimetry
(SLP), confocal scanning laser ophthalmoscopy (CSLO), etc., have been done
previously in [miki2012assessment]. The study found that although precision in
the measurement of structure is generally good with imaging techniques, there
was no uniform agreement regarding the most appropriate evaluation method.
Several studies have shown and argued the usefulness of OCT in glaucoma
progression detection due to its ability to visualize the retinal substructure
([geevarghese2021optical, bussel2014oct, abe2015use, tatham2017detecting]).
This is because of the ability of OCT to focus on the circumpapillary RNFL
(cpRNFL) thickness measurements, which are the most widely used parameters in
clinical practice. [abe2015use], [tatham2017detecting] and
[kotowski2011clinical] further reviewed different OCT assessment techniques
and found that the SDOCT has more precision and reproducibility of RNFL
measurements than other OCT methods, which is quintessential to evaluate
glaucoma progression, with consistently high sensitivity in detection.
Detection of glaucoma progression using different imaging techniques such as
SLP from GDx-VCC software ([dada2014scanning]), CSLO from Heidelberg Retina
Tomograph (HRT) ([maslin2015suppl]) exist. Still, again, agreement on the
methods needed to be found. Although not widely researched, researchers have
used fundus photos for glaucoma progression detection and found appreciative
results ([medeiros2021detection]). These methods highlight various challenges
in diagnosing glaucoma progression based on structural changes. These
challenges include differentiating between age-related variations and actual
glaucomatous changes, a decreased detection rate in advanced glaucoma (floor
effect), high costs of tests, and the need for specialized clinical expertise
to operate the equipment. Nevertheless, structural diagnostic methods continue
to be extensively researched because they provide detailed information about
the RNFL in Optic Discs. In the subsequent sections, we will explore various
methods and techniques researchers and clinicians have developed to identify
structural changes in glaucoma progression.

###  Clinical and Conventional Methods

In clinical practice, experts identify glaucoma progression by analyzing
changes in stereoscopic optic disc photographs. However, there’s often only
poor to moderate agreement in assessments between different experts
([o2010glaucomatous]). Like event and trend-based analysis in visual fields,
objective methods have also been used to evaluate glaucoma progression using
structural parameters. Similar to VF, GPA can be translated to structural
progression by measuring the RNFL test-retest variability with baseline
measurements where progression means exceeding a predetermined criterion of
this change. Research by [kaushik2015comparison], which compared GPA using SAP
parameters vs GPA using OCT measures, found that OCT detects progression in
early glaucoma better but performs poorly in advanced stages with overall RNFL
GPA obtaining lower sensitivity than SAP GPA. Because of these inconsistencies
and the continuous advancements in OCT technologies, the application of GPA
for structural evaluation is rare, and there is limited research in this area.
Trend analysis is another paradigm often explored for evaluating glaucoma
progression due to its ease of implementation and ability to track both
presence and rate of progression. A study by [lin2017trend] showed that cpRNFL
rates of change using the PLR are informative of VF loss in glaucoma but not
necessarily associated with VF defects. Several other research showed promise
for ganglion cell-inner plexiform layer (GCIPL) thinning rates being
applicable for objective assessment of glaucoma progression ([lee2017trend,
wu2020wide]). Another research comparing trend-based progression analysis
(TPA) with RNFL GPA found higher sensitivity for the TPA method - 48.8% vs
27.1% at 84.2% and 81.7% specificities respectively ([yu2016risk]). Using
global and cluster-wise linear regression, [lee2011trend] showed that the
localized OCT RNFL thinning rate also indicates progressive loss with high
sensitivity (62% at 95% specificity). In other research by
[thompson2021agreement], a poor agreement was found between trend-based and
qualitative assessment ($\kappa=0.0135-0.1222$), so the performance of trend
methods can be debated. Like functional progression, trend analysis using
structure also has the same limitations, requiring longer follow-ups for
accurate modeling ([miki2012assessment]) and is susceptible to age-related
loss ([jammal2020effect, leung2013impact]), etc.

###  Probabilistic Methods

Table 2 summarizes some methods to detect glaucoma progression using
structural parameters using advanced techniques, specifically focusing on
structural progression. In the following sections, we will discuss some of
these sophisticated methods and draw insights into how structural assessments
have shaped glaucoma progression detection. Regression-based approaches have
been studied in detail for predicting progression. A study by
[nagesh2019spatiotemporal] utilizing the Continuous Time-Hidden Markov Model
(CT-HMM) on a dataset of 135 eyes with SDOCT images showed impressive results
predicting future RNFL with a mean absolute error (MAE) of 3.48 vs 4.06 for
VFI over 4.9 years follow-up. This method notably surpassed the performance of
Linear Regression predictions. In [mohammadzadeh2021estimating], a
Hierarchical Longitudinal Bayesian Regression was applied on 112 eyes for
predicting GCC thickness, resulting in a notable negative correlation between
slope and baseline ranging from $r=-0.43to-0.50$ over 2-4.2 year follow-up. In
another study, [su2023spatially] incorporated spatially varying hierarchical
random effects on 111 eyes to predict progression, emphasizing that including
visit effects reduced estimation errors. This study demonstrated that the new
model obtains an accuracy of 21.4% vs. 18% to detect significant negative
slopes over the same follow-up duration with linear regression. Belghith et
al. explored several Bayesian methods to develop classification-based models
for glaucoma progression. In a study, [belghith2014joint] utilized the Fuzzy
Bayesian Detection Scheme (FBDS) on a training set of 25 eyes and a test set
of 117 eyes, achieving a 64% sensitivity at 94% specificity over three years.
[belghith2014glaucoma], in another study, showed an improved 70% sensitivity
at 94% specificity in its study over 2.2 years, indicating the Bayesian Fuzzy
Detection Scheme performs better in shorter follow-ups. The Variational Change
Analysis, as used in [belghith2013glaucoma] on a dataset of 267 eyes, obtained
a sensitivity of 86% at 96% specificity in the yet shorter follow-up of 0.5
years. In another research, [belghith2015learning] explored the Bayesian-
Kernel Detection Scheme and demonstrated a 78% sensitivity at 94% specificity
for non-progressing conditions in its 117-eye dataset over 1.7 years. Some
unsupervised models were also investigated for detecting progression.
[yousefi2015unsupervised] applied GEM on a large dataset of 2274 eyes with a
short five-week follow-up and achieved a sensitivity of 78% at 95%
specificity. Meanwhile, [huang2021detection] merged GEM with longitudinal
slopes on another massive dataset (3485 eyes), reported a 38.6% accuracy, and
showed that the ML model generally outperforms linear regression over numerous
follow-ups. The various probabilistic methods detailed in the studies for
detecting structural glaucoma progression have demonstrated considerable
promise. Many of these methods outperformed traditional linear regression
models, showcasing advancements in prediction accuracy, sensitivity, and
specificity. Regression and classification techniques achieved impressive
results on diverse datasets over varying follow-up durations. These structural
assessments are crucial as they offer direct, quantitative insights into
glaucoma progression, which can be complementary to functional assessments.
While functional assessment remains the clinical gold standard for monitoring
glaucoma progression, these probabilistic structural assessment techniques
underscore the potential of providing an earlier and possibly more nuanced
understanding of disease progression. Combining structural and functional
assessments may offer the most comprehensive view of glaucoma progression as
technology and algorithms advance.

###  Machine Learning Techniques

Machine Learning methods have shown significant potential in detecting
glaucoma progression through structural assessments. A study by
[balasubramanian2012localized] used Proper Orthogonal Decomposition (POD) for
regression on topographic measurements of HRT II from 246 eyes. This approach
could predict progression with high sensitivity and specificity, even for a
smaller follow-up. Another study by [christopher2018retinal], who applied
unsupervised learning using PCA combined with Logistic Regression on swept-
source OCT (SSOCT) images, showcased impressive AUC results, demonstrating its
efficiency in identifying structural progression. [mohammadzadeh2022detection]
similarly employed a multi-layer perceptron (MLP) classifier on macular OCT
Angiography (OCTA) images and showed that it outperforms logistic regression,
emphasizing machine learning’s capability in improving progression detection.
Comparatively, machine learning methods learn efficiently from intricate data
patterns, allowing for potentially higher predictive accuracy than
probabilistic methods. ML methods were found to be more proficient in handling
complex datasets and generally required lower follow-up time. Unlike the
previously discussed probabilistic methods, which primarily depend on
statistical models, ML techniques can easily extract meaningful features from
the data. However, a potential limitation might be that they require larger
datasets and computational power. While both probabilistic and ML methods
offer valuable insights, integrating both could lead to more holistic and
accurate detection of glaucomatous progression.

###  Deep Learning Approaches

Many Deep learning methods have been developed in glaucoma progression
detection, particularly in structural assessments. These methods are more
intricate than probabilistic and traditional ML models, and their results
often exceed the latter in both learning from complex data and accuracy. CNNs
are among the most common choices for researchers to learn spatial
representations from data. [mohammadzadeh2022detection] implemented a CNN
classifier on macular OCTA images from 134 patients taken over four visits
(2.4-5.5 years) and achieved an AUC of 0.81 vs 0.66 in logistic regression, a
significant performance improvement. Similarly, [mariottoni2023deep] applied a
CNN with an FC network on cpRNFL thickness from SDOCT from 816 eyes six visits
(in 3.5 years) and obtained an impressive 87.3% sensitivity at 86.4%
specificity. [mandal2023noise] developed a CNN-LSTM model based on SDOCT
B-scans from 3253 eyes across five follow-ups, predicting progression with a
48% sensitivity at 95% specificity. In a more specialized case, DL models have
been modified to use structural information uniquely to draw insights and
improve accuracy. [medeiros2021detection] developed a ResNet50 M2M model that
predicted RNFL thickness from fundus photos trained on a dataset with 8831
eyes (6.2 visits in 5.5 years). The subsequent trend-based analysis using the
RNFL thickness measure achieved an impressive AUC of 0.86. In another study,
[hassan2020conditional] used a Conditional Generative Adversarial Network
(GAN) on macular OCT volume scans to predict future glaucoma development in
OCT scans in 109 eyes. Researchers found that the generative model could
obtain better reconstruction on the 3rd visit than on the 2nd one.
[HOU2023854] implemented a Gated Transformer Networks (GTN) that analyzed RNFL
measurements from OCT scans of 4211 eyes and achieved an AUC of 0.97 with the
Majority Voting scheme. In another research, [bowd2021individualized]
integrated Deep Learning Autoencoders with cpRNFL thickness map data from the
OCT, which was able to identify progression with 90% sensitivity,
demonstrating that focusing on specific regions of interest can notably
improve predictive accuracy. DL methods often exhibit superior performance and
efficiency in processing large complex datasets compared to probabilistic and
ML techniques. Their capacity to extract complex salient features from image
datasets is a distinctive advantage. However, a limitation of this method is
that it often requires large datasets to generalize and significant
computational power to train. Nevertheless, with specialized models like M2M,
Conditional GAN, GTN, etc., DL methods can replace conventional glaucoma
progression detection practices, potentially minimizing the need for frequent
expert evaluations.

###  Time Series Approaches in Structural Assessment

Advanced algorithms and models are rapidly becoming essential in glaucoma
progression detection. For instance, the Gated Transformer Networks (GTN) from
[HOU2023854] utilizes OCT scans to offer holistic insight into progression,
signifying the growing importance of transformer architectures in medical
applications. The LSTM approach in [mandal2023noise] also stands out by
predicting progression using SD-OCT B-scans, highlighting the recurrent
model’s ability to capture temporal dependencies in ophthalmic data. These
sophisticated models underscore the potential of modern DL architectures to
provide both timely and accurate glaucoma progression assessments by
efficiently processing time-series information.  @ *11R ¿R @ A Review of
Methods for Detection and Prediction Glaucoma Progression using Structural
Assessments
[bl]Citation & [bl]Setup [bl]Algorithm/
Method [bl]Dataset [bl]Data
Type [bl]Follow-up
Period [bl]Reference
Standard [bl]Baseline
Method [bl]Model
Output [bl]Outcomes [bl]Summary
[nagesh2019spatiotemporal] Regression Cont. Time - Hidden Markov Model 135
eyes SDOCT Images and Avg RNFL Thickness 7.9 visits, 4.9 years VFI Progression
Linear Regression Predictions Change in RNFL Thickness Maps and VFI State 3.48
MAE RNFL and 4.06 MAE VFI OCT CT-HMM significantly outperforms LR and Avg RNFL
CT-HMM
[asaoka2021joint] Regression Latent Space LR - Deep Learning Cross-Sectional:
746 eyes, Longitudinal: Train 998 eyes, Test 148 eyes OCT Image data: GCC
thickness, macular RNFL, OS+RPE 8 VF tests, 5.6 OCT tests, 5.9 years RMSE and
Signigicant Slopes in LMM MLR, SVM, DL, CNN-TR, PLR, DLLR 68 points in HFA
10-2 test, 52 points in HFA 24-2 test RMSE cross-section 6.4dB, longitudinal
4.4dB and 3.7dB LSLR-DL predicts both cross-sectional and longitudinal VF
[medeiros2021detection] Regression ResNet50 (M2M model) 8831 eyes (Test: 1147
eyes) Color Fundus, SDOCT RNFL thickness 6.2 visits, 5.5 years statistical
significant negative slopes \- SDOCT global RNFL measurements, rate of change
in RNFL thickness AUROC 0.86 for predicting progressors M2M predicts RNFL
thickness and can monitor progression
[balasubramanian2012localized] Regression Proper Orthogonal Decomposition 246
eyes Topographic measurements of HRT II 4 follow-ups, 4.1 years SAP GPA or
stereo-photo assessment Topographic Component Analysis significant change
Progression: follow-ups observed positive rate greater than OPR 100%, 78%, 78%
sensitivity, 0%, 86%, 86% specificity POD with k-family-wise error rate
reduces number of follow-ups to predict progression
[mohammadzadeh2021estimating] Regression Hierarchical Longitudinal Bayesian
Regression 112 eyes GCC thickness from Macular OCT Scans 4 tests, 2-4.2 years
significant negative slope at Bayesian p<0.025 \- GCC thickness estimates,
macular slopes Negative correlation between slope and baseline: -0.43 to -0.50
Bayesian method is efficient method for estimating macular rates
[su2023spatially] Regression Spatially Varying Hierarchical Random Effects 111
eyes GCC thickness from Macular OCT Scans 4 tests, 2-4.2 years significant
slope when 95% CI is less or greater than 0 Simple Linear Regression GCC
thickness estimates, macular slopes 21.4% vs 18% signifiance negative slopes
Including visit effects reduces estimaton errors
[belghith2014joint] Classification Fuzzy Bayesian Detection Scheme Train - 25
eyes, Test - 117 eyes 3D SD-OCT Images 3 tests, 3 years stereo photograph
grading and VF GPA RNFL-SVM and RNFL-ANN Progressing ns Non-progressing 64%
Sensitivity at 94% Specificity FBDS using image features outperforms ANN and
SVM using RNFL classifiers
[belghith2014glaucoma] Classification Bayesian Fuzzy Detection Scheme 117 eyes
3D SDOCT voxel images 2 follow-ups, 2.2 years EMGT Criteria and Progression in
stereophoto RNFL-SVM, RNFL-ANN, MRF progressor or non-progressor 70%
sensitivity at 94% specificity BFDS has higher diagnostic accuracy
[belghith2013glaucoma] Classification Variational Change Analysis with
Markovian-a-priori 267 eyes Heidelberg Retina Tomograph (HRT II) 4 follow-ups,
0.5 years progressing by stereophoto or VF change Topographic CA, VCA
progressing vs non-progressing 86% sensitivity, 96% specificity Detection
formulated as missing data problem. VCA-MA outperforms other methods
[li2022deep] Classification DiagnoseNet and PredictNet 3003 train, 422 valid,
337 test for progression Color Fundus Photographs 34.8-41.7 months 3 VF points
worse than 5% baseline in 2 consecutive test \- progressing vs non-progressing
82% Sensitivity at 59% Specificity DL model useful in early detection of
glaucoma progression
[belghith2015learning] Classification Bayesian-Kernel Detection Scheme (BFDS)
117 eyes 3D SDOCT volume scans 2.21 tests, 1.7 years Longitudinal VF testing,
stereophoto assessment RNFL-SVM, RNFL-ANN, KDS, KDS, GBKDS, RBF-BKDS
progressing vs non-progressing 78% Sensitivity, 94% Specificity for non-
progressing BKDS outperforms other methods, Only healthy and non-progressing
eyes can produce high accuracy
[mariottoni2023deep] Classification CNN and FC Model 692 stable, 124
progressing eyes RNFL thickness peripapillary SDOCT 16.2 tests, 6 visits, 3.5
years Clinical judgements on longitudinal SDOCT reports trend-based analysis
progressing vs non-progressing 87.3% sensitivity, 86.4% specificity DL model
agreed with experts, provided likely location of change
[mohammadzadeh2022detection] Classification CNN and MLP classifiers 134
patients macular OCTA images 4 visits, 2.4-5.5 years significant negative MD
slope Logistic Regression progressing vs non-progressing AUC: 0.81 DL vs 0.66
with LR DL could extract and enhance progression detection
[HOU2023854] Classification Gated Transformer Networks (GTN) 4211 eyes (2666
patients) OCT scans 5 tests, 1.2 - 4.7 years significant negative slopes in
SAP trend-based methods, SAP GPA LMM, naive Bayes Classifiers progressing vs
non-progressing AUC: 0.97 with Majority Voting (M6) GTN outperforms
conventional methods; Ensemble methods improve performance
[mandal2023noise] Classification CNN-LSTM Classifier 3253 eyes SD-OCT B-scans
5 follow-ups, 3.1 years \- OLSLR on global mean RNFL thickness progressing vs
non-progressing 48% sensitivity at 95% specificity DL model identifies
structural progression from age-related changes without reference standard
[hassan2020conditional] Generative Conditional GAN: 3DCNN, UNet, PatchGAN 109
eyes macular OCT volume scan 4 tests, 6 months apart \- \- OCT Macular Volume
Scan, SSIM, PSNR 0.8325 SSIM with 3 visits, 0.8336 with 2 GAN can predict
future glaucoma development in OCT scans
[bowd2021individualized] Unsupervised DL Autoencoders 44 progressing, 303
nonprogressing, 109 healthy eyes cpRNFL thickness maps from OCT 4 visits, 2.9
years significant LMM slope for Region of Interest cpRNFL global slopes from
LMM Progressing: significant LMM slopes 90% sensitivity, progression slope
-1.28 µm/y ROI from DL-AE can boost progression accuracy
[christopher2018retinal] Unsupervised Principle Component Analysis with
Logistic Regression 179 eyes SSOCT images 7-8.7 tests, 1.7-2.2 years
significant negative slope LR with cpRNFL, SAP MD, FDT MD progressng vs non-
progressing AUC: 0.95 for RNFL PCA Computational method can identify
structural progression
[yousefi2015unsupervised] Unsupervised Gaussian Mixture Model (GEM) 2274 eyes
RNFL Thickness from SD-OCT 5 tests, 5 weeks event-based SAP GPA Linear
regression progressing vs non-progressing 78% sensitivity, 95% specificity GEM
predicts RNFL patterns for glaucoma progression
[huang2021detection] Unsupervised GEM + Longitudinal Slopes 3485 eyes RNFL
thickness maps from OCT scans 9 follow-ups statistical significant negative
slopes Linear Regression stable vs progressed 38.6% accuracy with ML model ML
model predicts progression better than LR

##  3 Structure-Function Relationship in Glaucoma Progression Detection

###  Clinical Methods for the Assessment of Glaucoma Progression Using
Structure and Function

As discussed previously, several studies indicated that OCT is more sensitive
than VF in the early stages of glaucoma progression detection, but this
sensitivity decreases as progression advances ([zhang2017comparison,
abe2016relative]). Some research ([swaminathan2021rapid,
gracitelli2015association]) exploring the effect of progressive RNFL loss
found an association with loss in visual fields. Similar conclusions were
drawn from studies where changes in macular thickness were used to find an
association with central visual field loss ([mohammadzadeh2020longitudinal]).
Another research by [suda2018evaluation] found an appreciative correlation
(R=0.589) between SAP and OCT results for glaucoma progression. In a more
novel approach, both the structure and function were used to obtain rates of
change of RGC count as indicators for neural damage in glaucoma progression,
paving the way for combined structure-function index ([medeiros2012structure,
hirooka2016estimating, wu2021simplified]). A survey by [lisboa2013combining],
which analyzed several studies in the same field, found that a combined
approach is more effective in detecting glaucoma progression even though there
can be disagreements between detection using structural or functional measures
alone. [gardiner2012effect] further found that even though the structure-
function is still affected by inter-test variability, it is still feasible to
combine them for progression assessment.

###  Sophisticated Methods to Detect Glaucoma Progression Using Combined
Structure and Function

Studies in probabilistic methods for glaucoma progression detection using
combined structure-function demonstrated improved predictive accuracy in
general (Table 3). [medeiros2011combining], showed in their research using the
Bayesian Hierarchical Model on 434 glaucoma-suspected eyes that probabilistic
methods obtain higher sensitivity with VFI and temporal, superior, nasal,
inferior, and temporal (TSNIT) RNFL averages than traditional OLS regression
standards. Similar results were also seen in [medeiros2012combining], which
used the Bayesian Joint Regression Model and applied to 242 eyes, which
outperformed the OLS linear regression in predicting MD and rim area (RA) from
SAP and CSLO data. In another study, [russell2012improved] implemented
Bayesian Linear Regression on 179 eyes, which indicated higher performance for
short time series, but traditional OLSLR was more accurate for longer follow-
ups when analyzing VF. Overall, Bayesian methods have shown superior
performance, though the input data type and follow-up time might influence
detection accuracy for progression. Following the studies on probabilistic
methods, researchers have focused on developing innovative statistical methods
for detecting glaucoma progression, leveraging both structural and functional
indicators. In research by [bilonick2008evaluating], Latent Class Regression
was developed, which was able to identify accurately baseline RNFL
characteristics that indicate progression. Meanwhile, the study by
[hu2014prediction] employed a Dynamic Structure-Function (DSF) model, which
demonstrated an improved performance over the conventional OLS regression,
especially in shorter follow-ups. Drawing insights from glaucoma suspects, a
study by [meira2013predicting] showed the advantages of the Joint Longitudinal
Survival Model (JLSM) with RGC Estimation, which outperformed estimation with
structure or function measurements alone. [medeiros2014evaluation], analyzing
492 eyes, reiterated this by linking progressive RA loss conclusively to VF
loss, combining JLSM with RA and VF to predict progression. Further,
[zhalechian2022augmenting] in their study with a Kalman Filter Estimator for
estimating future MD with RNFL measures indicated that global RNFL just
marginally enhanced MD predictions as compared to utilizing MD by itself,
showing confounding results. These statistical methods illustrate that a
holistic, combined structural and functional analysis is paramount for
accurate detection of glaucoma progression. Time-series forecasting methods,
such as the DSF model, showed the importance of shorter follow-up for
detection and were helpful in frequent monitoring. However, when compared with
the probabilistic Bayesian approaches, these methods, while promising, do show
that the Bayesian techniques often have better sensitivity. Thus, it is
essential to choose an appropriate model in clinical settings to detect
progression based on the type of data and how long the patient has been
monitored.

###  Artificial Intelligence Utilising Combined Structure and Function
Relationship to Evaluate Glaucoma Progression

Machine learning techniques, especially when they combine both structural and
functional data, have shown better results in identifying glaucoma
progression. In classification methods, [nouri2021prediction] research used an
Elastic net logistic regression and ML classifier to achieve AUCs between 0.79
and 0.81, suggesting an improvement of predictive power using baseline and
longitudinal structural data on visual field (VF) progression. This was
reflected by multiple studies notably [bowd2012predicting] using Relevance
Vector Machine, [yousefi2013glaucoma] implementing a multitude of classifiers
(including Bayesian Net and Lazy K Star), and [lee2020machine] employing
Random Forest and Extra Trees, where all emphasized the significant role of
RNFL measurements and baseline parameters for detecting progression. Notably,
the study by [kamalipour2023combining] showed an impressive AUC of 0.89 by
integrating OCT and OCTA features via a Gradient Boosting Classifier,
demonstrating its efficacy in forecasting clinical VF progression. On the
other hand, regression methods by [lee2022predictive] using Random Forest to
measure the rate of change in RNFL thickness against baseline emphasized how
such models improve the predictive accuracy of baseline ONH characteristics.
While the outcomes are varied, Random Forest, Gradient Boosting, and Elastic
Net methods tend to produce superior performance, often surpassing traditional
regression and decision trees. It is crucial to note that these studies used
diverse data types, from cpRNFL and GCIPL thickness to OCT scans and VF
parameters, allowing for nuanced analysis. While these methods were shown to
be great at identifying complex patterns in the data, there’s still a worry
that they might overfit smaller datasets. Therefore, while ML methods provide
a holistic view, a balance between model complexity and data characteristics
is essential for optimal results. Deep Learning techniques, being able to
learn from complex data, provide advanced modeling capacity, especially when
leveraging both structure and function. A study by [sedai2020forecasting] used
a 3DCNN with traditional ML forecasting techniques, primarily utilizing cpRNFL
from OCT, age, and 24-2 VF tests as input, which obtained the lowest MAE
across healthy, suspect, and glaucoma subjects, showcasing the method’s
reliability in real-world applications. [lee2021predicting] in their research
introduced an innovative Machine to Machine (M2M) method paired with JLSM,
which used Color Fundus and SDOCT RNFL thickness data to detect longitudinal
changes, showed this method can distinguish between converter and non-
converter groups in glaucoma. In a notable multimodal approach,
[pham2023multimodal] combined ResNET50 and LSTM to forecast future VF points
using the 30-2 VF HFA SITA and RNFL thickness map to demonstrate its
predictive accuracy in noisy data environments. Among these, the incorporation
of time-series data, as seen in LSTMs, highlights a paradigm shift towards
capturing sequential data and temporal dependencies, improving prediction
accuracy. However, while DL methods have shown superior predictive
capabilities, the need for larger datasets for generalization and
computational power for training is a significant drawback. Moreover, DL,
being a black-box model, cannot provide interpretable results, especially in
clinical settings where understanding model decisions is crucial. Elaborating
on the DL methods, techniques like 3DCNNs, M2M methods, and complex
architectures like ResNET50 with LSTM have been adopted efficiently to detect
glaucoma progression. These models utilize large datasets to extract intricate
patterns from both structural and functional data to produce improved
performance. In comparison, traditional ML models, like Random Forest and
Gradient Boosting, although sophisticated, often require a certain degree of
manual feature extraction and may not capture intricacies as effectively as DL
models. When compared with probabilistic and purely statistical models, such
as PLR methods or linear regressions, both machine and deep learning offer
superior predictive capabilities than the former. However, probabilistic and
statistical methods provide interpretable results and a clear insight into
data dynamics, often making them preferred choices for direct interpretability
and better understanding of the data.  @ *11R ¿R @ A Review of Methods for
Detection and Prediction Glaucoma Progression using both Structure and
Function
[bl]Citation & [bl]Setup [bl]Algorithm/
Method [bl]Dataset [bl]Data
Type [bl]Follow-up
Period [bl]Reference
Standard [bl]Baseline
Method [bl]Model
Output [bl]Outcomes [bl]Summary
[bilonick2008evaluating] Regression Latent Class Regression 106 eyes MD, PSD,
AGIS score, VFI, RNFL 5 tests, 5 years significant negative slope (p<0.05) \-
LCR Model slope, AIC 2494.8 AIC Baseline RNFL was indicative of progression
[medeiros2011combining] Regression Bayesian Hierarchical Model 434 eyes -
glaucoma and suspect VFI from SAP, GDx TSNIT average from Optic Disc
Stereophotographs, Scanning Laser Polarimerty 3 reliable tests, 4.2 years OLS
regression slope of VFI, observer disagreement of stereophotograph change OLS
regression slopes on VFI and TSNIT with p <0.05 Bayesian slopes of change for
VFI and TSNIT 22.7% vs 12.8% using VFI, 74% vs 37% with optic disc at 100%
specificity Bayesian method obtains significantly higher sensitivity for
progression with VFI and TSNIT 100% specificity
[hu2014prediction] Regression Dynamic Structure-Function Model 220 eyes from
DIGS and ADAGES MS from 24-2 SAP Tests and Rim Area from scanning laser
ophthalmoscopy 3 follow-ups, 8.4 years Glaucoma Criteria based on ADAGES OLS
regression slopes Prediction in future visits and MSE Lower MSE 70% eyes at
visit 4 and 60% for 5,6,7 DSF outperforms OLS in shorter intervals
[medeiros2012combining] Regression Bayesian Joint Regression Model 242 eyes -
glaucoma MD from SAP Test, RA from CSLO 4 follow-ups, 6.4 years PSD <0.05 in
SAP tests wrt Baseline and reproducible at least once OLS linear regression
slopes with MD and RA Bayesian regression slopes of change with MD and RA 5.13
vs 11.2 MSE predicting MD and 0.016 vs 0.027 MSE predicting RA Bayesian method
outperforms OLS
[meira2013predicting] Regression Joint Longitudinal Survival Model (JLSM) with
RGC Estimation 288 glaucoma suspect eyes Avg RNFL from OCT and MD from SAP 4
follow-up, 3.8 years Significant Slopes from JLSM Isolated Structure or
Function Measurements Combined Structue Function Index, RGC Estimates -18,987
cells/y progressors, -8,808 cells/y non progressors Joint Longitudinal
Esitmates better than Structure or Function alone
[russell2012improved] Regression Bayesian Linear Regression 179 eyes MS from
24-2 VF tests, RA from CLST HRT 8 follow-ups, 5.8 years Significant negative
slope (p<0.05) OLSLR Rate of change in MS RMSE 0.14dB smaller than OLSLR BLR
with MS and RA outperforms OLSLR for short time series, OLSLR more accurate
for long time series with only VF
[sedai2020forecasting] Regression 3D CNN + ML Forcasting Train: 859 subjects,
Test: 230 subjects cpRNFL from OCT, age, IOP, 24-2 VF tests 3 visits, 3.65
years Glaucoma (2 consecutive test ONL) and abnormal ONH Linear Trend Based
Estimation RNFL Global and Sectoral Means MAE: 1.10, 1.79, 1.87 for healthy,
suspect and glaucoma Model consistent across suspect and glaucoma subjects
[medeiros2014evaluation] Regression JLSM with RA and VF 492 eyes suspect RA
from CSLO, SAP 24-2 VF, IOP 5 CSLO tests, 2 years 3 consecutive abnormal VF
tests with PSD (p<0.05) \- RA loss rate, Survival adapted R2, proportion of
treatment effect (PTE) R2 62% univariate model, 81% multivariate, PTE 65%
Progressive RA loss predictive of VF loss
[lee2021predicting] Regression Machine to Machine (M2M) with JLSM 1072 eyes
Color Fundus, SDOCT RNFL thickness, 24-2 SAP tests 4.2 fundus tests, 9.6 VF
tests, 5.9 years 2 consecutive abnormal VF (PSD with p<0.05) \- progression:
significant slopes from M2M predictions -1.02 um/y vs -0.67um/y between
converter and non-converter Longitudinal changes from DL can predict
progression
[pham2023multimodal] Regression ResNET50 and LSTM Train: 266 eyes, Test: 99
eyes 30-2 VF HFA SITA tests, RNFL map from cirrus OCT tests Train: 5.7 visits,
5 years; Test: 2.3 visits, 3.3 years \- \- Future VF points MAE 3.31, RMSE
4.58 VF and OCT data improves predictive performance, model is useful with
noisy data
[zhalechian2022augmenting] Regression Kalman Filter Estimator 362 subjects
demographics, IOP, VF MD, VF PSD, global RNFLT 10.6, 19.9, 11.7 follow-ups,
13.6, 12.1, 5.7 years \- OLS Linear Regression future MD, PSD and RNFL values
Predictive accuracy: 73.5% vs 58% with LR global RNFL minimally improved MD
prediction than with MD alone
[lee2022predictive] Regression Random Forest 712 eyes with OAG demographics,
IOP, LCCI, peripapillary CT, global RNFLT, VF MD, PSD, AXL, CCT 5.3-11.5 years
\- Regression and Decision Trees Rate of change in RNFL Thickness wrt Baseline
MAE: 0.075, 0.115, 0.128 for RF, LR, DT baseline ONH characteristics predict
risk of faster progression
[yousefi2013glaucoma] Classification ML Classifiers: Bayesian Net, Lazy K
Star, Meta Classifiers, AD Tree, CART 107 eyes - progressing, 72 - stable 52 +
MD + PSD VF SAP Points, Global + Sectoral OCT RNFL Thickness 4.3 follow-ups,
2.2 years PGON criteria + GPA \- progressed vs non-progressed 0.88 AUC for
Random Forest with RNFL and SAP; 0.88 AUC for Lazy K Star with only RNFL RNFL
measurements provide more discriminating power
[lee2020machine] Classification Random Forest and Extra Trees Train: 110 eyes,
Test: 45 eyes IOP, CCT, 30-2 SAP VF tests, mGCIL thickness, cpRNFL thickness
HDOCT 6.2 follow-ups, 3.39 years event based GPA Linear Regression Slopes
progressing vs non-progressing 0.881 AUC for Extra Trees, 0.811 AUC for Random
Forest ML Classifiers can predict progression effectively in young myopic
patients
[nouri2021prediction] Classification Elastic Net Logistic Regression, MLC 104
eyes cpRNFL, GCIPL thickness from Macular SD-OCT 5 tests, 3years PLR
deterioration on 24-2 SAP tests <-1dB/yr, p<0.01 PLR Method progressing vs
non-progressing AUC 0.79 with ENR, 0.81 with ML VF progression can be
predicted from baseline and longitudinal structural data
[bowd2012predicting] Classification Relevance Vector Machine (RVM) 264 eyes
(10 fold CV) 117 CSLO points from HRT II and 52 SAP points from 24-1 SITA HFA
II 5.35 years SAP GPA or stereo-photo assessment Glaucoma Probability Score
progressing vs non-progressing AUC: 0.640, 0.762, 0.805 using CSLO, SAP,
combined parameters RVM with baseline parameters predicts future progression
[kamalipour2023combining] Classification Gradient Boosting (GB) Classifier 110
eyes OCT scans, OCTA scans 3 follow-ups, 4.1 years SAP GPA, significant
negative slope of VF MD, PPLR event \- progressing vs non-progressing AUC 0.89
with both OCT and OCTA features ML with OCT and OCTA predicts clinical VF
progression

##  4 Glaucoma Progression Detection with EHR and Clinical Data

Deep learning has also been successfully applied to detect glaucoma
progression using structured clinical data and text notes from Electronic
Health Records (EHR). These techniques offer a paradigm shift from traditional
uses of deep learning methods using structure or functional progression
characteristics and have been shown to predict progression accurately.
Research by [wang2023machine] and [baxter2019machine] explores several machine
learning classifiers using EHR data to predict if the data is indicative of
progressive characteristics and if the patient needs surgery. The models
obtained a moderate AUC of 0.623-0.673 on the test sets, suggesting further
research is required during inference, especially for different demographics.
Another research [tao2023predicting] by the same group studied survival-based
AI algorithms to predict if a patient is showing characteristics of
glaucomatous progression to surgery. They showed that using more complex
algorithms such as DeepSurv (DL method) has a better predictive AUC of 0.802
due to its ability to capture information from high-dimensional data. The
addition of clinical text notes with EHR data has also boosted the performance
of deep learning models in classifying whether patients undergoing surgeries
show signs of progression, obtaining an AUC of 0.873
([jalamangala2023predicting]). Exploring the utility of DL methods to predict
progression from unstructured data, a Natural Language Processing (NLP) based
DL algorithm was developed to predict progression from free-text clinical
notes ([wang2022deep]). Although a combination of free text and EHR data
obtained a higher AUC of 0.73 in the NLP model, only free-text data also had
an appreciative AUC of 0.70. Making the DL model more complex has been shown
to enhance its accuracy. For example, research by [hu2022predicting]
demonstrated this by using BERT-based models on clinical notes obtained from
ophthalmologists. This approach predicted which patients might need glaucoma
surgery with a reliability score, AUC of 0.734. Although the performance was
not better than DL methods with just structured EHR data, this research showed
the potential of using massive pre-trained models to predict progression using
free text notes, which are abundantly available.

##  5 Conclusion

In this chapter, we have seen various models and algorithms using different
modalities or combinations of modalities to detect, predict, and forecast
glaucoma progression from longitudinal data. We observed various advantages of
using complex techniques, trade-offs between structure and function, and
challenges in the modeling of medical data. Identifying a holistic method for
predicting glaucoma progression with a reliable reference standard is found to
be quintessential. In addition, trade-offs between structural and functional
assessment for progression suggest the need for a precise, reproducible, and
comprehensive feature set that can accurately represent glaucomatous
characteristics with the ability to detect and separate age-related
variability. Obtaining longitudinal image data can be time-consuming and
expensive, which adds another layer of constraints to the model development.
However, with the advent of complex models such as deep learning, the
availability of computational resources and data from large cohorts can be
used to make powerful models that can predict glaucoma progression accurately
and with minimal clinical expertise. In subsequent chapters, we introduce and
formulate novel deep-learning strategies for detecting glaucoma progression
within a longitudinal cohort characterized by data ambiguity, leveraging
anatomical information of progression characteristics.