Role of Deep Learning-Quantified Hyperreflective Foci for the Prediction of Geographic Atrophy Progression

Disease activity and therapeutic response to pegcetacoplan for geographic atrophy identified by deep learning-based analysis of OCT

PURPOSE

To quantify morphological changes of the photoreceptors (PR) and retinal pigment epithelium (RPE) layers under pegcetacoplan therapy in geographic atrophy (GA) using deep learning-based analysis of optical coherence tomography (OCT) images.

DESIGN

Post-hoc longitudinal image analysis SUBJECTS: Patients with GA due to age-related macular degeneration from two prospective randomized phase III clinical trials (OAKS and DERBY) METHODS: Deep learning-based segmentation of RPE loss and PR degeneration, defined as loss of the ellipsoid zone (EZ) layer on OCT, over 24 months on SD-OCT images MAIN OUTCOME MEASURES: Change in the mean area of RPE loss and EZ loss over time in the pooled sham arms and the monthly (PM)/every other month (PEOM) treatment arms RESULTS: 897 eyes of 897 patients were included. There was a therapeutic reduction of RPE loss growth by 22%/20% in OAKS and 27%/21% in DERBY for PM/PEOM compared to sham, respectively, at 24 months. The reduction on the EZ level was significantly higher with 53%/46% in OAKS and 47%/46% in DERBY for PM/PEOM compared to sham at 24 months. The baseline EZ-RPE difference had an impact on disease activity and therapeutic response. The therapeutic benefit for RPE loss growth increased with larger EZ-RPE difference quartiles from 21.9%, 23.1%, 23.9% to 33.6% for PM vs. sham (all p<0.01) and from 13.6% (p=0.11), 23.8%, 23.8% to 20.0% for PEOM vs. sham (p<0.01) in quartiles 1,2,3 and 4, respectively, at 24 months. Regarding EZ layer maintenance, the therapeutic reduction of loss increased from 14.8% (p=0.09), 33.3%, 46.6% to 77.8% (p<0.0001) between PM and sham and from 15.9% (p=0.08), 33.8%, 52.0% to 64.9% (p<0.0001) between PEOM and sham for quartiles 1-4 at 24 months.

CONCLUSION

OCT-based AI analysis objectively identifies and quantifies PR and RPE degeneration in GA. Reductions in further PR degeneration consistent with EZ loss on OCT are even higher than the effect on RPE loss in phase 3 trials of pegcetacoplan treatment. The EZ-RPE difference has a strong impact on disease progression and therapeutic response. Identification of patients with higher EZ-RPE loss difference may become an important criterion for the management of GA secondary to AMD.

Risk Factors for Worsening Morphology and Visual Acuity in Eyes with Adult-Onset Foveomacular Vitelliform Dystrophy

PURPOSE

To explore clinical risk factors and OCT features associated with worse visual acuity (VA), progression of disease, choroidal neovascularization (CNV), and atrophy in eyes with adult-onset foveomacular vitelliform dystrophy (AOFVD).

DESIGN

Single-center, retrospective, observational cohort study.

PARTICIPANTS

Patients seen at Duke Eye Center between January 2012 and May 2023 with a diagnosis of AOFVD confirmed via OCT and fundus autofluorescence.

METHODS

Baseline and final-visit images from eyes with AOFVD were examined. Disease stage was assigned, and presence of atrophy or CNV was determined. Clinical and OCT features associated with progression to atrophy and CNV were determined using t tests and chi-square analysis. Correlation with lower VA was determined using linear regression.

MAIN OUTCOME MEASURES

Association of clinical characteristics and OCT features with worse VA, progression of disease, CNV, and atrophy as determined by independent t tests, chi-square analysis, and linear regression (P < 0.05).

RESULTS

One hundred one eyes (63 patients) met inclusion criteria for this study, with mean follow-up duration of 48 months (standard deviation, 31 months). Fifty-one percent of eyes progressed beyond baseline staging during follow-up; among baseline stage 1 eyes, incidence of atrophy was 0.068/person-year; incidence of CNV was 0.022/person-year. Risk factors for worse final VA were baseline presence of vitreomacular traction ([VMT], P = 0.006), ellipsoid zone attenuation (P = 0.02), and increased lesion height and width (P < 0.001). Predictors of progression include diabetes mellitus (P = 0.01), statin use (P = 0.03), presence of hyperreflective foci (P = 0.01), and increased lesion width and volume (P = 0.03 and P = 0.04, respectively). Predictors of atrophy include the baseline presence of VMT (P = 0.02), decreased choroidal thickness (P = 0.03), and greater maximal height, width, and volume of the lesion (P = 0.03, P = 0.02, and P = 0.009, respectively). Lower baseline VA (P = 0.03) and increased lesion volume (P = 0.04) were associated with CNV.

CONCLUSIONS

Clinical and OCT imaging features at baseline may prove useful in stratifying patient risk for progression, atrophy, CNV, and worse VA. Features such as statin use, diabetes, baseline VA, and laterality should be accounted for. OCT features, such as lesion size, VMT, ellipsoid zone attenuation, choroidal thickness, and hyperreflective foci, may impart greater risk of poor outcomes. Future prospective analysis accounting for the time to development of atrophy and CNV is needed.

FINANCIAL DISCLOSURES

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

[Use of artificial intelligence in geographic atrophy in age-related macular degeneration]

The first regulatory approval of treatment for geographic atrophy (GA) secondary to age-related macular degeneration in the USA constitutes an important milestone; however, due to the nature of GA as a non-acute, insidiously progressing pathology, the ophthalmologist faces specific challenges concerning risk stratification, making treatment decisions, monitoring of treatment and patient education. Innovative retinal imaging modalities, such as fundus autofluorescence (FAF) and optical coherence tomography (OCT) have enabled identification of typical morphological alterations in relation to GA, which are also suitable for the quantitative characterization of GA. Solutions based on artificial intelligence (AI) enable automated detection and quantification of GA-specific biomarkers on retinal imaging data, also retrospectively and over time. Moreover, AI solutions can be used for the diagnosis and segmentation of GA as well as the prediction of structure and function without and under GA treatment, thereby making a valuable contribution to treatment monitoring and the identification of high-risk patients and patient education. The integration of AI solutions into existing clinical processes and software systems enables the broad implementation of informed and personalized treatment of GA secondary to AMD.

Combination of optical coherence tomography-derived shape and texture features are associated with development of sub-foveal geographic atrophy in dry AMD

Geographic atrophy (GA) is an advanced form of dry age-related macular degeneration (AMD) that leads to progressive and irreversible vision loss. Identifying patients with greatest risk of GA progression is important for targeted utilization of emerging therapies. This study aimed to comprehensively evaluate the role of shape-based fractal dimension features ( F fd ) of sub-retinal pigment epithelium (sub-RPE) compartment and texture-based radiomics features ( F t ) of Ellipsoid Zone (EZ)-RPE and sub-RPE compartments for risk stratification for subfoveal GA (sfGA) progression. This was a retrospective study of 137 dry AMD subjects with a 5-year follow-up. Based on sfGA status at year 5, eyes were categorized as Progressors and Non-progressors. A total of 15 shape-based F fd of sub-RPE surface and 494 F t from each of sub-RPE and EZ-RPE compartments were extracted from baseline spectral domain-optical coherence tomography scans. The top nine features were identified from F fd and F t feature pool separately using minimum Redundancy maximum Relevance feature selection and used to train a Random Forest (RF) classifier independently using three-fold cross validation on the training set ( S t , N = 90) to distinguish between sfGA Progressors and Non-progressors. Combined F fd and F t was also evaluated in predicting risk of sfGA progression. The RF classifier yielded AUC of 0.85, 0.79 and 0.89 on independent test set ( S v , N = 47) using F fd , F t , and their combination, respectively. Using combined F fd and F t , the improvement in AUC was statistically significant on S v with p-values of 0.032 and 0.04 compared to using only F fd and only F t , respectively. Combined F fd and F t appears to identify high-risk patients. Our results show that FD and texture features could be potentially used for predicting risk of sfGA progression and future therapeutic response.

Deep Learning-Based Prediction of Individual Geographic Atrophy Progression from a Single Baseline OCT

Objective

To identify the individual progression of geographic atrophy (GA) lesions from baseline OCT images of patients in routine clinical care.

Design

Clinical evaluation of a deep learning-based algorithm.

Subjects

One hundred eighty-four eyes of 100 consecutively enrolled patients.

Methods

OCT and fundus autofluorescence (FAF) images (both Spectralis, Heidelberg Engineering) of patients with GA secondary to age-related macular degeneration in routine clinical care were used for model validation. Fundus autofluorescence images were annotated manually by delineating the GA area by certified readers of the Vienna Reading Center. The annotated FAF images were anatomically registered in an automated manner to the corresponding OCT scans, resulting in 2-dimensional en face OCT annotations, which were taken as a reference for the model performance. A deep learning-based method for modeling the GA lesion growth over time from a single baseline OCT was evaluated. In addition, the ability of the algorithm to identify fast progressors for the top 10%, 15%, and 20% of GA growth rates was analyzed.

Main Outcome Measures

Dice similarity coefficient (DSC) and mean absolute error (MAE) between manual and predicted GA growth.

Results

The deep learning-based tool was able to reliably identify disease activity in GA using a standard OCT image taken at a single baseline time point. The mean DSC for the total GA region increased for the first 2 years of prediction (0.80-0.82). With increasing time intervals beyond 3 years, the DSC decreased slightly to a mean of 0.70. The MAE was low over the first year and with advancing time slowly increased, with mean values ranging from 0.25 mm to 0.69 mm for the total GA region prediction. The model achieved an area under the curve of 0.81, 0.79, and 0.77 for the identification of the top 10%, 15%, and 20% growth rates, respectively.

Conclusions

The proposed algorithm is capable of fully automated GA lesion growth prediction from a single baseline OCT in a time-continuous fashion in the form of en face maps. The results are a promising step toward clinical decision support tools for therapeutic dosing and guidance of patient management because the first treatment for GA has recently become available.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Sequence of Morphological Changes Preceding Atrophy in Intermediate AMD Using Deep Learning

Purpose

Investigating the sequence of morphological changes preceding outer plexiform layer (OPL) subsidence, a marker preceding geographic atrophy, in intermediate AMD (iAMD) using high-precision artificial intelligence (AI) quantifications on optical coherence tomography imaging.

Methods

In this longitudinal observational study, individuals with bilateral iAMD participating in a multicenter clinical trial were screened for OPL subsidence and RPE and outer retinal atrophy. OPL subsidence was segmented on an A-scan basis in optical coherence tomography volumes, obtained 6-monthly with 36 months follow-up. AI-based quantification of photoreceptor (PR) and outer nuclear layer (ONL) thickness, drusen height and choroidal hypertransmission (HT) was performed. Changes were compared between topographic areas of OPL subsidence (AS), drusen (AD), and reference (AR).

Results

Of 280 eyes of 140 individuals, OPL subsidence occurred in 53 eyes from 43 individuals. Thirty-six eyes developed RPE and outer retinal atrophy subsequently. In the cohort of 53 eyes showing OPL subsidence, PR and ONL thicknesses were significantly decreased in AS compared with AD and AR 12 and 18 months before OPL subsidence occurred, respectively (PR: 20 µm vs. 23 µm and 27 µm [P < 0.009]; ONL, 84 µm vs. 94 µm and 98 µm [P < 0.008]). Accelerated thinning of PR (0.6 µm/month; P < 0.001) and ONL (0.8 µm/month; P < 0.001) was observed in AS compared with AD and AR. Concomitant drusen regression and hypertransmission increase at the occurrence of OPL subsidence underline the atrophic progress in areas affected by OPL subsidence.

Conclusions

PR and ONL thinning are early subclinical features associated with subsequent OPL subsidence, an indicator of progression toward geographic atrophy. AI algorithms are able to predict and quantify morphological precursors of iAMD conversion and allow personalized risk stratification.

The Total Macular Burden of Hyperreflective Foci and the Onset of Persistent Choroidal Hypertransmission Defects in Intermediate AMD

PURPOSE

The association between the total macular burden of hyperreflective foci (HRF) in eyes with intermediate AMD (iAMD) and the onset of persistent choroidal hypertransmission defects (hyperTDs) was studied using swept-source optical coherence tomography (SS-OCT).

DESIGN

Post hoc subgroup analysis of a prospective study.

METHODS

A retrospective review of iAMD eyes from subjects enrolled in a prospective SS-OCT study was performed. All eyes underwent 6×6 mm SS-OCT angiography (SS-OCTA) imaging at baseline and follow-up visits. En face sub-retinal pigment epithelium (subRPE) slabs with segmentation boundaries positioned 64 to 400 µm beneath Bruch's membrane (BM) were used to identify persistent choroidal hyperTDs. None of the eyes had persistent hyperTDs at baseline. The same subRPE slab was used to identify choroidal hypotransmission defects (hypoTDs) attributable to HRF located either intraretinally (iHRF) or along the RPE (rpeHRF) based on corresponding B-scans. A semiautomated algorithm was used by 2 independent graders to validate and refine the HRF outlines. The HRF area and the drusen volume within a 5 mm fovea-centered circle were measured at each visit.

RESULTS

The median follow-up time for the 171 eyes from 121 patients included in this study was 59.1 months (95% CI: 52.0-67.8 months). Of these, 149 eyes (87%) had HRF, and 82 (48%) developed at least one persistent hyperTD during the follow-up. Although univariable Cox regression analyses showed that both drusen volume and total HRF area were associated with the onset of the first persistent hyperTD, multivariable analysis showed that the area of total HRF was the sole significant predictor for the onset of hyperTDs (P < .001). ROC analysis identified an HRF area ≥ 0.07 mm² to predict the onset of persistent hyperTDs within 1 year with an area under the curve (AUC) of 0.661 (0.570-0.753), corresponding to a sensitivity of 55% and a specificity of 74% (P < .001).

CONCLUSIONS

The total macular burden of HRF, which includes both the HRF along the RPE and within the retina, is an important predictor of disease progression from iAMD to the onset of persistent hyperTDs and should serve as a key OCT biomarker to select iAMD patients at high risk for disease progression in future clinical trials.

Structural OCT and OCT angiography biomarkers associated with the development and progression of geographic atrophy in AMD

BACKGROUND

Geographic atrophy (GA) is an advanced, irreversible, and progressive form of age-related macular degeneration (AMD). Structural optical coherence tomography (OCT) and OCT angiography (OCTA) have been largely used to characterize this stage of AMD and, more importantly, to define biomarkers associated with the development and progression of GA in AMD.

METHODS

Articles pertaining to OCT and OCTA biomarkers related to the development and progression of GA with relevant key words were used to search in PubMed, Researchgate, and Google Scholar. The articles were selected based on their relevance, reliability, publication year, published journal, and accessibility.

RESULTS

Previous reports have highlighted various OCT and OCTA biomarkers linked to the onset and advancement of GA. These biomarkers encompass characteristics such as the size, volume, and subtype of drusen, the presence of hyperreflective foci, basal laminar deposits, incomplete retinal pigment epithelium and outer retinal atrophy (iRORA), persistent choroidal hypertransmission defects, and the existence of subretinal drusenoid deposits (also referred to as reticular pseudodrusen). Moreover, biomarkers associated with the progression of GA include thinning of the outer retina, photoreceptor degradation, the distance between retinal pigment epithelium and Bruch's membrane, and choriocapillaris loss.

CONCLUSION

The advent of novel treatment strategies for GA underscores the heightened need for prompt diagnosis and precise monitoring of individuals with this condition. The utilization of structural OCT and OCTA becomes essential for identifying distinct biomarkers associated with the initiation and progression of GA.

Rediscovering Age-Related Macular Degeneration with Swept-Source OCT Imaging: The 2022 Charles L. Schepens, MD, Lecture

PURPOSE

Swept-source OCT angiography (SS-OCTA) scans of eyes with age-related macular degeneration (AMD) were used to replace color, autofluorescence, infrared reflectance, and dye-based fundus angiographic imaging for the diagnosis and staging of AMD. Through the use of different algorithms with the SS-OCTA scans, both structural and angiographic information can be viewed and assessed using both cross sectional and en face imaging strategies.

DESIGN

Presented at the 2022 Charles L. Schepens, MD, Lecture at the American Academy of Ophthalmology Retina Subspecialty Day, Chicago, Illinois, on September 30, 2022.

PARTICIPANTS

Patients with AMD.

METHODS

Review of published literature and ongoing clinical research using SS-OCTA imaging in AMD.

MAIN OUTCOME MEASURES

Swept-source OCT angiography imaging of AMD at different stages of disease progression.

RESULTS

Volumetric SS-OCTA dense raster scans were used to diagnose and stage both exudative and nonexudative AMD. In eyes with nonexudative AMD, a single SS-OCTA scan was used to detect and measure structural features in the macula such as the area and volume of both typical soft drusen and calcified drusen, the presence and location of hyperreflective foci, the presence of reticular pseudodrusen, also known as subretinal drusenoid deposits, the thickness of the outer retinal layer, the presence and thickness of basal laminar deposits, the presence and area of persistent choroidal hypertransmission defects, and the presence of treatment-naïve nonexudative macular neovascularization. In eyes with exudative AMD, the same SS-OCTA scan pattern was used to detect and measure the presence of macular fluid, the presence and type of macular neovascularization, and the response of exudation to treatment with vascular endothelial growth factor inhibitors. In addition, the same scan pattern was used to quantitate choriocapillaris (CC) perfusion, CC thickness, choroidal thickness, and the vascularity of the choroid.

CONCLUSIONS

Compared with using several different instruments to perform multimodal imaging, a single SS-OCTA scan provides a convenient, comfortable, and comprehensive approach for obtaining qualitative and quantitative anatomic and angiographic information to monitor the onset, progression, and response to therapies in both nonexudative and exudative AMD.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Visual acuity prediction on real-life patient data using a machine learning based multistage system

In ophthalmology, intravitreal operative medication therapy (IVOM) is a widespread treatment for diseases related to the age-related macular degeneration (AMD), the diabetic macular edema, as well as the retinal vein occlusion. However, in real-world settings, patients often suffer from loss of vision on time scales of years despite therapy, whereas the prediction of the visual acuity (VA) and the earliest possible detection of deterioration under real-life conditions is challenging due to heterogeneous and incomplete data. In this contribution, we present a workflow for the development of a research-compatible data corpus fusing different IT systems of the department of ophthalmology of a German maximum care hospital. The extensive data corpus allows predictive statements of the expected progression of a patient and his or her VA in each of the three diseases. For the disease AMD, we found out a significant deterioration of the visual acuity over time. Within our proposed multistage system, we subsequently classify the VA progression into the three groups of therapy "winners", "stabilizers", and "losers" (WSL classification scheme). Our OCT biomarker classification using an ensemble of deep neural networks results in a classification accuracy (F1-score) of over 98%, enabling us to complete incomplete OCT documentations while allowing us to exploit them for a more precise VA modelling process. Our VA prediction requires at least four VA examinations and optionally OCT biomarkers from the same time period to predict the VA progression within a forecasted time frame, whereas our prediction is currently restricted to IVOM/no therapy. We achieve a final prediction accuracy of 69% in macro average F1-score, while being in the same range as the ophthalmologists with 57.8 and 50 ± 10.7 % F1-score.

Recurrent and Concurrent Prediction of Longitudinal Progression of Stargardt Atrophy and Geographic Atrophy

Stargardt disease and age-related macular degeneration are the leading causes of blindness in the juvenile and geriatric populations, respectively. The formation of atrophic regions of the macula is a hallmark of the end-stages of both diseases. The progression of these diseases is tracked using various imaging modalities, two of the most common being fundus autofluorescence (FAF) imaging and spectral-domain optical coherence tomography (SD-OCT). This study seeks to investigate the use of longitudinal FAF and SD-OCT imaging (month 0, month 6, month 12, and month 18) data for the predictive modelling of future atrophy in Stargardt and geographic atrophy. To achieve such an objective, we develop a set of novel deep convolutional neural networks enhanced with recurrent network units for longitudinal prediction and concurrent learning of ensemble network units (termed ReConNet) which take advantage of improved retinal layer features beyond the mean intensity features. Using FAF images, the neural network presented in this paper achieved mean (± standard deviation, SD) and median Dice coefficients of 0.895 (± 0.086) and 0.922 for Stargardt atrophy, and 0.864 (± 0.113) and 0.893 for geographic atrophy. Using SD-OCT images for Stargardt atrophy, the neural network achieved mean and median Dice coefficients of 0.882 (± 0.101) and 0.906, respectively. When predicting only the interval growth of the atrophic lesions with FAF images, mean (± SD) and median Dice coefficients of 0.557 (± 0.094) and 0.559 were achieved for Stargardt atrophy, and 0.612 (± 0.089) and 0.601 for geographic atrophy. The prediction performance in OCT images is comparably good to that using FAF which opens a new, more efficient, and practical door in the assessment of atrophy progression for clinical trials and retina clinics, beyond widely used FAF. These results are highly encouraging for a high-performance interval growth prediction when more frequent or longer-term longitudinal data are available in our clinics. This is a pressing task for our next step in ongoing research.

Artificial intelligence in retinal imaging: current status and future prospects

INTRODUCTION

The steadily growing and aging world population, in conjunction with continuously increasing prevalences of vision-threatening retinal diseases, is placing an increasing burden on the global healthcare system. The main challenges within retinology involve identifying the comparatively few patients requiring therapy within the large mass, the assurance of comprehensive screening for retinal disease and individualized therapy planning. In order to sustain high-quality ophthalmic care in the future, the incorporation of artificial intelligence (AI) technologies into our clinical practice represents a potential solution.

AREAS COVERED

This review sheds light onto already realized and promising future applications of AI techniques in retinal imaging. The main attention is directed at the application in diabetic retinopathy and age-related macular degeneration. The principles of use in disease screening, grading, therapeutic planning and prediction of future developments are explained based on the currently available literature.

EXPERT OPINION

The recent accomplishments of AI in retinal imaging indicate that its implementation into our daily practice is likely to fundamentally change the ophthalmic healthcare system and bring us one step closer to the goal of individualized treatment. However, it must be emphasized that the aim is to optimally support clinicians by gradually incorporating AI approaches, rather than replacing ophthalmologists.

Point-to-point associations of drusen and hyperreflective foci volumes with retinal sensitivity in non-exudative age-related macular degeneration

OBJECTIVES

To evaluate the quantitative impact of drusen and hyperreflective foci (HRF) volumes on mesopic retinal sensitivity in non-exudative age-related macular degeneration (AMD).

METHODS

In a standardized follow-up scheme of every three months, retinal sensitivity of patients with early or intermediate AMD was assessed by microperimetry using a custom pattern of 45 stimuli (Nidek MP-3, Gamagori, Japan). Eyes were consecutively scanned using Spectralis SD-OCT (20° × 20°, 1024 × 97 × 496). Fundus photographs obtained by the MP-3 allowed to map the stimuli locations onto the corresponding OCT scans. The volume and mean thickness of drusen and HRF within a circle of 240 µm centred at each stimulus point was determined using automated AI-based image segmentation algorithms.

RESULTS

8055 individual stimuli from 179 visits from 51 eyes of 35 consecutive patients were matched with the respective OCT images in a point-to-point manner. The patients mean age was 76.85 ± 6.6 years. Mean retinal sensitivity at baseline was 25.7 dB. 73.47% of all MP-spots covered drusen area and 2.02% of MP-spots covered HRF. A negative association between retinal sensitivity and the volume of underlying drusen (p < 0.001, Estimate -0.991 db/µm3) and HRF volume (p = 0.002, Estimate -5.230 db/µm3) was found. During observation time, no eye showed conversion to advanced AMD.

CONCLUSION

A direct correlation between drusen and lower sensitivity of the overlying photoreceptors can be observed. For HRF, a small but significant correlation was shown, which is compromised by their small size. Biomarker quantification using AI-methods allows to determine the impact of sub-clinical features in the progression of AMD.

Multimodal imaging and deep learning in geographic atrophy secondary to age-related macular degeneration

Geographic atrophy (GA) secondary to age-related macular degeneration is among the most common causes of irreversible vision loss in industrialized countries. Recently, two therapies have been approved by the US FDA. However, given the nature of their treatment effect, which primarily involves a relative decrease in disease progression, discerning the individual treatment response at the individual level may not be readily apparent. Thus, clinical decision-making may have to rely on the quantification of the slope of GA progression before and during treatment. A panel of imaging modalities and artificial intelligence (AI)-based algorithms are available for such quantifications. This article aims to provide a comprehensive overview of the fundamentals of GA imaging, the procedures for diagnosis and classification using these images, and the cutting-edge role of AI algorithms in automatically deriving diagnostic and prognostic insights from imaging data.

Choroidal Hyper-Reflective Foci in Geographic Atrophy

Purpose

The purpose of this study was to describe the presence of choroidal hyper-reflective foci (HRF) on optical coherence tomography (OCT) in patients with geographic atrophy (GA). The relationship between the presence and quantity of choroidal HRF and other clinical and imaging factors was also investigated.

Methods

A total of 40 participants (40 eyes) with GA and age-related macular degeneration (AMD) were retrospectively analyzed. OCT images were reviewed for the presence, characteristics, and localization of choroidal HRF. The amount of choroidal HRF was quantified in different choroidal layers by two different (i.e. threshold reflectivity and manual counting) methodologies. The primary outcome was to describe and quantify choroidal HRF and correlate them with GA lesion size.

Results

Structural OCT images showed that all patients had multiple hyper-reflective deposits in different layers of the choroid. These hyper-reflective deposits in the choroid were located near Bruch's membrane or the edges of the blood vessels, particularly in the Sattler's layer, and none were observed inside the vessels. Choroidal HRF exhibited variable size and shape and varying effects on the posterior signal, including shadowing or hypertransmission. Mean ± SD number of choroidal HRF per B-scan was 21.5 ± 15.4 using the threshold reflectivity methodology and 25.1 ± 16.0 using the manual counting methodology. A significant correlation between the untransformed GA size and number of HRF was found, considering both quantitative strategies.

Conclusions

Hyper-reflective dots in the choroid of subjects with GA may be readily identified with structural OCT. These HRF might represent a natural component of the choroid that becomes more visible due to the absence of the retinal pigment epithelium.

Diagnosis and Management of Patients With Geographic Atrophy Secondary to Age-Related Macular Degeneration: A Delphi Consensus Exercise

Geographic atrophy (GA) is a progressive and irreversible retinal disease with no comprehensive recommendations for diagnosis or monitoring. We used a Delphi approach to determine consensus in key areas around diagnosis and management of GA. A steering committee of eight retina specialists developed two sequential online surveys administered to eye care professionals (ECPs). Consensus was defined as agreement by ≥ 75% of respondents. Up to 177 ECPs from eight countries completed one or both surveys. Consensus was achieved in several topics related to diagnostic imaging, including the use of optical coherence tomography, and the urgent need for treatments and beneficial interventions to reduce the associated burden. Currently, low-vision aids and smoking cessation are considered the most beneficial interventions. We demonstrate consensus for diagnosis and management of patients with GA including best practices in patient identification and monitoring, and unmet needs. [Ophthalmic Surg Lasers Imaging Retina 2023;54:589-598.].

Novel Fractal-Based Sub-RPE Compartment OCT Radiomics Biomarkers Are Associated With Subfoveal Geographic Atrophy in Dry AMD

OBJECTIVE

The purpose of this study was to quantitatively characterize the shape of the sub-retinal pigment epithelium (sub-RPE, i.e., space bounded by RPE and Bruch's membrane) compartment on SD-OCT using fractal dimension (FD) features and evaluate their impact on risk of subfoveal geographic atrophy (sfGA) progression.

METHODS

This was an IRB-approved retrospective study of 137 subjects with dry age-related macular degeneration (AMD) with subfoveal GA. Based on sfGA status at year five, eyes were categorized as "Progressors" and "Non-progressors". FD analysis allows quantification of the degree of shape complexity and architectural disorder associated with a structure. To characterize the structural irregularities along the sub-RPE surface between the two groups of patients, a total of 15 shape descriptors of FD were extracted from the sub-RPE compartment of baseline OCT scans. The top four features were identified using minimum Redundancy maximum Relevance (mRmR) feature selection method and evaluated with Random Forest (RF) classifier using three-fold cross validation from the training set (N = 90). Classifier performance was subsequently validated on the independent test set (N = 47).

RESULTS

Using the top four FD features, a RF classifier yielded an AUC of 0.85 on the independent test set. Mean fractal entropy (p-value = 4.8e-05) was identified as the most significant biomarker; higher values of entropy being associated with greater shape disorder and risk for sfGA progression.

CONCLUSIONS

FD assessment holds promise for identifying high-risk eyes for GA progression.

SIGNIFICANCE

With further validation, FD features could be potentially used for clinical trial enrichment and assessments for therapeutic response in dry AMD patients.

Geographic Atrophy in Age-Related Macular Degeneration: A Tale of Two Stages

Purpose

To examine disease progression in age-related macular degeneration (AMD) at 2 distinct stages, progression to geographic atrophy (GA) versus GA expansion, by comparison of the risk and protective factors at each stage.

Design

Perspective.

Subjects

Individuals at risk of GA or with GA.

Main Outcome Measures

Progression to GA and GA expansion rate.

Methods

Critical synthesis of the literature on risk and protective factors, both environmental and genetic, for progression to GA versus GA expansion in AMD.

Results

Comparison of the risk and protective factors demonstrates partially overlapping but partially distinct risk and protective factors for progression to GA versus GA expansion. Some factors are shared (i.e., operating in the same direction at both stages), others are not shared, and others seem to operate in different directions at each stage. Risk variants at ARMS2/HTRA1 increase both risk of progression to GA and GA expansion rate, presumably through the same mechanism. By contrast, risk and protective variants at CFH/CFHR alter risk of GA but not GA expansion rate. A risk variant at C3 increases risk of GA but is associated with slower GA expansion. In environmental factors, cigarette smoking is associated with increased risk of GA and faster GA expansion, whereas increased age is associated with the former but not the latter. The Mediterranean diet is associated with decreased progression at both stages, although the food components with the largest contributions seem to differ between the 2 stages. Some phenotypic features, such as reticular pseudodrusen and hyperreflective foci, are associated with increased progression at both stages.

Conclusions

Analysis of the risk and protective factors for progression to GA and GA expansion demonstrates partially overlapping but partially distinct elements at each stage: some are shared, some are relevant to 1 stage only, and some even seem active in opposite directions at each stage. Aside from ARMS2/HTRA1, the overlap between the genetic risk factors for the 2 stages is minimal. This suggests that the biologic mechanisms differ at least partially between the 2 disease stages. This has implications for therapeutic approaches and suggests that treatment aimed at the underlying disease processes may need to be tailored by stage.

Financial Disclosures

Proprietary or commercial disclosure may be found after the references.

Detection of macular atrophy in age-related macular degeneration aided by artificial intelligence

INTRODUCTION

Age-related macular degeneration (AMD) is a leading cause of irreversible visual impairment worldwide. The endpoint of AMD, both in its dry or wet form, is macular atrophy (MA) which is characterized by the permanent loss of the RPE and overlying photoreceptors either in dry AMD or in wet AMD. A recognized unmet need in AMD is the early detection of MA development.

AREAS COVERED

Artificial Intelligence (AI) has demonstrated great impact in detection of retinal diseases, especially with its robust ability to analyze big data afforded by ophthalmic imaging modalities, such as color fundus photography (CFP), fundus autofluorescence (FAF), near-infrared reflectance (NIR), and optical coherence tomography (OCT). Among these, OCT has been shown to have great promise in identifying early MA using the new criteria in 2018.

EXPERT OPINION

There are few studies in which AI-OCT methods have been used to identify MA; however, results are very promising when compared to other imaging modalities. In this paper, we review the development and advances of ophthalmic imaging modalities and their combination with AI technology to detect MA in AMD. In addition, we emphasize the application of AI-OCT as an objective, cost-effective tool for the early detection and monitoring of the progression of MA in AMD.

Clinical validation for automated geographic atrophy monitoring on OCT under complement inhibitory treatment

Geographic atrophy (GA) represents a late stage of age-related macular degeneration, which leads to irreversible vision loss. With the first successful therapeutic approach, namely complement inhibition, huge numbers of patients will have to be monitored regularly. Given these perspectives, a strong need for automated GA segmentation has evolved. The main purpose of this study was the clinical validation of an artificial intelligence (AI)-based algorithm to segment a topographic 2D GA area on a 3D optical coherence tomography (OCT) volume, and to evaluate its potential for AI-based monitoring of GA progression under complement-targeted treatment. 100 GA patients from routine clinical care at the Medical University of Vienna for internal validation and 113 patients from the FILLY phase 2 clinical trial for external validation were included. Mean Dice Similarity Coefficient (DSC) was 0.86 ± 0.12 and 0.91 ± 0.05 for total GA area on the internal and external validation, respectively. Mean DSC for the GA growth area at month 12 on the external test set was 0.46 ± 0.16. Importantly, the automated segmentation by the algorithm corresponded to the outcome of the original FILLY trial measured manually on fundus autofluorescence. The proposed AI approach can reliably segment GA area on OCT with high accuracy. The availability of such tools represents an important step towards AI-based monitoring of GA progression under treatment on OCT for clinical management as well as regulatory trials.

Automated Identification and Segmentation of Ellipsoid Zone At-Risk Using Deep Learning on SD-OCT for Predicting Progression in Dry AMD

BACKGROUND

The development and testing of a deep learning (DL)-based approach for detection and measurement of regions of Ellipsoid Zone (EZ) At-Risk to study progression in nonexudative age-related macular degeneration (AMD).

METHODS

Used in DL model training and testing were 341 subjects with nonexudative AMD with or without geographic atrophy (GA). An independent dataset of 120 subjects were used for testing model performance for prediction of GA progression. Accuracy, specificity, sensitivity, and intraclass correlation coefficient (ICC) for DL-based EZ At-Risk percentage area measurement was calculated. Random forest-based feature ranking of EZ At-Risk was compared to previously validated quantitative OCT-based biomarkers.

RESULTS

The model achieved a detection accuracy of 99% (sensitivity = 99%; specificity = 100%) for EZ At-Risk. Automatic EZ At-Risk measurement achieved an accuracy of 90% (sensitivity = 90%; specificity = 84%) and the ICC compared to ground truth was high (0.83). In the independent dataset, higher baseline mean EZ At-Risk correlated with higher progression to GA at year 5 (p < 0.001). EZ At-Risk was a top ranked feature in the random forest assessment for GA prediction.

CONCLUSIONS

This report describes a novel high performance DL-based model for the detection and measurement of EZ At-Risk. This biomarker showed promising results in predicting progression in nonexudative AMD patients.

The Role of Inflammation in Age-Related Macular Degeneration: Updates and Possible Therapeutic Approaches

Age-related macular degeneration (AMD) is a common retinal disease characterized by complex pathogenesis and extremely heterogeneous characteristics. Both in "dry" and "wet" AMD forms, the inflammation has a central role to promote the degenerative process and to stimulate the onset of complications. AMD is characterized by several proinflammatory stimuli, cells and mediators involved, and metabolic pathways. Nowadays, inflammatory biomarkers may be unveiled and analyzed by means of several techniques, including laboratory approaches, histology, immunohistochemistry, and noninvasive multimodal retinal imaging. These methodologies allowed to perform remarkable steps forward for understanding the role of inflammation in AMD pathogenesis, also offering new opportunities to optimize the diagnostic workup of the patients and to develop new treatments. The main goal of the present paper is to provide an updated scenario of the current knowledge regarding the role of inflammation in "dry" and "wet" AMD and to discuss new possible therapeutic strategies.

Quantitative Autofluorescence in Non-Neovascular Age Related Macular Degeneration

Quantitative autofluorescence (qAF8) level is a presumed surrogate marker of lipofuscin content in the retina. We investigated the changes in the qAF8 levels in non-neovascular AMD. In this prospective cohort study, Caucasians aged ≥50 years with varying severity of non-neovascular AMD in at least one eye and Snellen visual acuity ≥6/18 were recruited. The qAF8 levels were analysed in the middle eight segments of the Delori pattern (HEYEX software, Heidelberg, Germany). The AMD categories were graded using both the Beckman classification and multimodal imaging (MMI) to include the presence of subretinal drusenoid deposits (SDD). A total of 353 eyes from 231 participants were analyzed. Compared with the age-matched controls, the qAF8 values decreased in the eyes with AMD (adjusted % difference = -19.7% [95% CI -28.8%, -10.4%]; p < 0.001) and across the AMD categories, (adjusted % differences; Early, -13.1% (-24.4%, -1%), p = 0.04; intermediate AMD (iAMD), -22.9% (-32.3%, -13.1%), p < 0.001; geographic atrophy -25.2% (-38.1%, -10.4%), p = 0.002). On MMI, the qAF8 was reduced in the AMD subgroups relative to the controls, (adjusted % differences; Early, -5.8% (-18.9%, 8.3%); p = 0.40; iAMD, -26.7% (-36.2%, -15.6%); p < 0.001; SDD, -23.7% (-33.6%, -12.2%); p < 0.001; atrophy, -26.7% (-39.3%, -11.3%), p = 0.001). The qAF8 levels declined early in AMD and were not significantly different between the severity levels of non-neovascular AMD, suggesting the early and sustained loss of function of the retinal pigment epithelium in AMD.

Melanophages give rise to hyperreflective foci in AMD, a disease-progression marker

Retinal melanosome/melanolipofuscin-containing cells (MCCs), clinically visible as hyperreflective foci (HRF) and a highly predictive imaging biomarker for the progression of age-related macular degeneration (AMD), are widely believed to be migrating retinal pigment epithelial (RPE) cells. Using human donor tissue, we identify the vast majority of MCCs as melanophages, melanosome/melanolipofuscin-laden mononuclear phagocytes (MPs). Using serial block-face scanning electron microscopy, RPE flatmounts, bone marrow transplantation and in vitro experiments, we show how retinal melanophages form by the transfer of melanosomes from the RPE to subretinal MPs when the "don't eat me" signal CD47 is blocked. These melanophages give rise to hyperreflective foci in Cd47^-/--mice in vivo, and are associated with RPE dysmorphia similar to intermediate AMD. Finally, we show that Cd47 expression in human RPE declines with age and in AMD, which likely participates in melanophage formation and RPE decline. Boosting CD47 expression in AMD might protect RPE cells and delay AMD progression.

Associations of systemic health and medication use with the enlargement rate of geographic atrophy in age-related macular degeneration

BACKGROUND

The associations of geographic atrophy (GA) progression with systemic health status and medication use are unclear.

METHODS

We manually delineated GA in 318 eyes in the Age-Related Eye Disease Study. We calculated GA perimeter-adjusted growth rate as the ratio between GA area growth rate and mean GA perimeter between the first and last visit for each eye (mean follow-up=5.3 years). Patients' history of systemic health and medications was collected through questionnaires administered at study enrolment. We evaluated the associations between GA perimeter-adjusted growth rate and 27 systemic health factors using univariable and multivariable linear mixed-effects regression models.

RESULTS

In the univariable model, GA perimeter-adjusted growth rate was associated with GA in the fellow eye at any visit (p=0.002), hypertension history (p=0.03), cholesterol-lowering medication use (p<0.001), beta-blocker use (p=0.02), diuretic use (p<0.001) and thyroid hormone use (p=0.03). Among the six factors, GA in the fellow eye at any visit (p=0.008), cholesterol-lowering medication use (p=0.002), and diuretic use (p<0.001) were independently associated with higher GA perimeter-adjusted growth rate in the multivariable model. GA perimeter-adjusted growth rate was 51.1% higher in patients with versus without cholesterol-lowering medication use history and was 37.8% higher in patients with versus without diuretic use history.

CONCLUSIONS

GA growth rate may be associated with the fellow eye status, cholesterol-lowering medication use, and diuretic use. These possible associations do not infer causal relationships, and future prospective studies are required to investigate the relationships further.

Quantitative approaches in multimodal fundus imaging: State of the art and future perspectives

When it first appeared, multimodal fundus imaging revolutionized the diagnostic workup and provided extremely useful new insights into the pathogenesis of fundus diseases. The recent addition of quantitative approaches has further expanded the amount of information that can be obtained. In spite of the growing interest in advanced quantitative metrics, the scientific community has not reached a stable consensus on repeatable, standardized quantitative techniques to process and analyze the images. Furthermore, imaging artifacts may considerably affect the processing and interpretation of quantitative data, potentially affecting their reliability. The aim of this survey is to provide a comprehensive summary of the main multimodal imaging techniques, covering their limitations as well as their strengths. We also offer a thorough analysis of current quantitative imaging metrics, looking into their technical features, limitations, and interpretation. In addition, we describe the main imaging artifacts and their potential impact on imaging quality and reliability. The prospect of increasing reliance on artificial intelligence-based analyses suggests there is a need to develop more sophisticated quantitative metrics and to improve imaging technologies, incorporating clear, standardized, post-processing procedures. These measures are becoming urgent if these analyses are to cross the threshold from a research context to real-life clinical practice.

Predicting Topographic Disease Progression and Treatment Response of Pegcetacoplan in Geographic Atrophy Quantified by Deep Learning

PURPOSE

To identify disease activity and effects of intravitreal pegcetacoplan treatment on the topographic progression of geographic atrophy (GA) secondary to age-related macular degeneration quantified in spectral-domain OCT (SD-OCT) by automated deep learning assessment.

DESIGN

Retrospective analysis of a phase II clinical trial study evaluating pegcetacoplan in GA patients (FILLY, NCT02503332).

SUBJECTS

SD-OCT scans of 57 eyes with monthly treatment, 46 eyes with every-other-month (EOM) treatment, and 53 eyes with sham injection from baseline and 12-month follow-ups were included, in a total of 312 scans.

METHODS

Retinal pigment epithelium loss, photoreceptor (PR) integrity, and hyperreflective foci (HRF) were automatically segmented using validated deep learning algorithms. Local progression rate (LPR) was determined from a growth model measuring the local expansion of GA margins between baseline and 1 year. For each individual margin point, the eccentricity to the foveal center, the progression direction, mean PR thickness, and HRF concentration in the junctional zone were computed. Mean LPR in disease activity and treatment effect conditioned on these properties were estimated by spatial generalized additive mixed-effect models.

MAIN OUTCOME MEASURES

LPR of GA, PR thickness, and HRF concentration in μm.

RESULTS

A total of 31,527 local GA margin locations were analyzed. LPR was higher for areas with low eccentricity to the fovea, thinner PR layer thickness, or higher HRF concentration in the GA junctional zone. When controlling for topographic and structural risk factors, we report on average a significantly lower LPR by -28.0% (95% confidence interval [CI], -42.8 to -9.4; P = 0.0051) and -23.9% (95% CI, -40.2 to -3.0; P = 0.027) for monthly and EOM-treated eyes, respectively, compared with sham.

CONCLUSIONS

Assessing GA progression on a topographic level is essential to capture the pathognomonic heterogeneity in individual lesion growth and therapeutic response. Pegcetacoplan-treated eyes showed a significantly slower GA lesion progression rate compared with sham, and an even slower growth rate toward the fovea. This study may help to identify patient cohorts with faster progressing lesions, in which pegcetacoplan treatment would be particularly beneficial. Automated artificial intelligence-based tools will provide reliable guidance for the management of GA in clinical practice.

Reverse engineering for reconstructing baseline features of dry age-related macular degeneration in optical coherence tomography

Age-related macular degeneration (AMD) is the most widespread cause of blindness and the identification of baseline AMD features or biomarkers is critical for early intervention. Optical coherence tomography (OCT) imaging produces a 3D volume consisting of cross sections of retinal tissue while fundus fluorescence (FAF) imaging produces a 2D mapping of retina. FAF has been a good standard for assessing dry AMD late-stage geographic atrophy (GA) while OCT has been used for assessing early AMD biomarkers beyond as well. However, previous approaches in large extent defined AMD features subjectively based on clinicians' observation. Deep learning-an objective artificial intelligence approach, may enable to discover 'true' salient AMD features. We develop a novel reverse engineering approach which bases on the backbone of a fully convolutional neural network to objectively identify and visualize AMD early biomarkers in OCT from baseline exams before significant atrophy occurs. Utilizing manually annotated GA regions on FAF from a follow-up visit as ground truth, we segment GA regions and reconstruct early AMD features in baseline OCT volumes. In this preliminary exploration, compared with ground truth, we achieve baseline GA segmentation accuracy of 0.95 and overlapping ratio of 0.65. The reconstructions consistently highlight that large druse and druse clusters with or without mixed hyper-reflective focus lesion on baseline OCT cause the conversion of GA after 12 months. However, hyper-reflective focus lesions and subretinal drusenoid deposit lesions alone are not seen such conversion after 12 months. Further research with larger dataset would be needed to verify these findings.

Fundus autofluorescence and optical coherence tomography biomarkers associated with the progression of geographic atrophy secondary to age-related macular degeneration

OBJECTIVES

To investigate the impact of qualitatively graded and deep learning quantified imaging biomarkers on growth of geographic atrophy (GA) secondary to age-related macular degeneration.

METHODS

This prospective study included 1062 visits of 181 eyes of 100 patients with GA. Spectral-domain optical coherence tomography (SD-OCT) and fundus autofluorescence (FAF) images were acquired at each visit. Hyperreflective foci (HRF) were quantitatively assessed in SD-OCT volumes using a validated deep learning algorithm. FAF images were graded for FAF patterns, subretinal drusenoid deposits (SDD), GA lesion configuration and atrophy enlargement. Linear mixed models were calculated to investigate associations between all parameters and GA progression.

RESULTS

FAF patterns were significantly associated with GA progression (p < 0.001). SDD was associated with faster GA growth (p = 0.005). Eyes with higher HRF concentrations showed a trend towards faster GA progression (p = 0.072) and revealed a significant impact on GA enlargement in interaction with FAF patterns (p = 0.01). The fellow eye status had no significant effect on lesion enlargement (p > 0.05). The diffuse-trickling FAF pattern exhibited significantly higher HRF concentrations than any other pattern (p < 0.001).

CONCLUSION

Among a wide range of investigated biomarkers, SDD and FAF patterns, particularly in interaction with HRF, significantly impact GA progression. Fully automated quantification of retinal imaging biomarkers such as HRF is both reliable and merited as HRF are indicators of retinal pigment epithelium dysmorphia, a central pathogenetic mechanism in GA. Identifying disease markers using the combination of FAF and SD-OCT is of high prognostic value and facilitates individualized patient management in a clinical setting.

Automated segmentation and feature discovery of age-related macular degeneration and Stargardt disease via self-attended neural networks

Age-related macular degeneration (AMD) and Stargardt disease are the leading causes of blindness for the elderly and young adults respectively. Geographic atrophy (GA) of AMD and Stargardt atrophy are their end-stage outcomes. Efficient methods for segmentation and quantification of these atrophic lesions are critical for clinical research. In this study, we developed a deep convolutional neural network (CNN) with a trainable self-attended mechanism for accurate GA and Stargardt atrophy segmentation. Compared with traditional post-hoc attention mechanisms which can only visualize CNN features, our self-attended mechanism is embedded in a fully convolutional network and directly involved in training the CNN to actively attend key features for enhanced algorithm performance. We applied the self-attended CNN on the segmentation of AMD and Stargardt atrophic lesions on fundus autofluorescence (FAF) images. Compared with a preexisting regular fully convolutional network (the U-Net), our self-attended CNN achieved 10.6% higher Dice coefficient and 17% higher IoU (intersection over union) for AMD GA segmentation, and a 22% higher Dice coefficient and a 32% higher IoU for Stargardt atrophy segmentation. With longitudinal image data having over a longer time, the developed self-attended mechanism can also be applied on the visual discovery of early AMD and Stargardt features.

Deep learning: applications in retinal and optic nerve diseases

Deep learning (DL) represents a paradigm-shifting, burgeoning field of research with emerging clinical applications in optometry. Unlike traditional programming, which relies on human-set specific rules, DL works by exposing the algorithm to a large amount of annotated data and allowing the software to develop its own set of rules (i.e. learn) by adjusting the parameters inside the model (network) during a training process in order to complete the task on its own. One major limitation of traditional programming is that, with complex tasks, it may require an extensive set of rules to accurately complete the assignment. Additionally, traditional programming can be susceptible to human bias from programmer experience. With the dramatic increase in the amount and the complexity of clinical data, DL has been utilised to automate data analysis and thus to assist clinicians in patient management. This review will present the latest advances in DL, for managing posterior eye diseases as well as DL-based solutions for patients with vision loss.

Long Term Time-Lapse Imaging of Geographic Atrophy: A Pilot Study

Geographic atrophy (GA), the late stage of age-related macular degeneration, is a major cause of visual disability whose pathophysiology remains largely unknown. Modern fundus imaging and histology revealed the complexity of the cellular changes that accompanies atrophy. Documenting the activity of the disease in the margins of atrophy, where the transition from health to disease occurs, would contribute to a better understanding of the progression of GA. Time-lapse imaging facilitates the identification of structural continuities in changing environments. In this retrospective pilot study, we documented the long-term changes in atrophy margins by time-lapse imaging of infrared scanning laser ophthalmoscopy (SLO) and optical coherence tomography (OCT) images in 6 cases of GA covering a mean period of 32.8 months (range, 18–72). The mean interval between imaging sessions was 2.4 months (range, 1.4–3.8). By viewing time-lapse sequences we observed extensive changes in the pattern of marginal hyperreflective spots, which associated fragmentation, increase and/or disappearance. Over the entire span of the follow-up, the most striking changes were those affecting hyperreflective spots closest to margins of atrophy, on the non-atrophic side of the retina; a continuum between the successive positions of some of the hyperreflective spots was detected, both by SLO and OCT. This continuum in their successive positions resulted in a subjective impression of a centrifugal motion of hyperreflective spots ahead of atrophy progression. Such mobilization of hyperreflective spots was detected up to several hundred microns away from atrophic borders. Such process is likely to reflect the inflammatory and degenerative process underlying GA progression and hence deserves further investigations. These results highlight the interest of multimodal time-lapse imaging to document cell-scale dynamics during progression of GA.

The effect of pegcetacoplan treatment on photoreceptor maintenance in geographic atrophy monitored by AI-based OCT analysis

PURPOSE

To investigate the therapeutic effect of intravitreal pegcetacoplan on the inhibition of photoreceptor (PR) loss and thinning in geographic atrophy (GA) on conventional spectral domain-optical coherence tomography (SD-OCT) imaging by deep learning-based automated PR quantification.

DESIGN

Post-hoc analysis of a prospective, multicenter, randomized, sham-controlled, masked phase II trial investigating the safety and efficacy of pegcetacoplan for the treatment of GA due to age-related macular degeneration.

PARTICIPANTS

Study eyes of 246 patients, randomized 1:1:1 to monthly (AM), bimonthly (AEOM) and sham (SM) treatment.

METHODS

We performed fully automated, deep learning-based segmentation of retinal pigment epithelium (RPE) loss and PR thickness on SD-OCT volumes acquired at baseline, month 2, 6 and 12. The difference in the change of PR loss area was compared between treatment arms. Change in PR thickness adjacent to the GA borders and in the whole 20 degrees scanning area was compared between treatment arms.

MAIN OUTCOME MEASURES

Square root transformed PR loss area in μm or mm, PR thickness in μm, PR loss/RPE loss ratio.

RESULTS

A total of 31,556 B-Scans of 644 SD-OCT volumes of 161 study eyes (AM: 52, AEOM: 54, SM: 56) were evaluated from baseline to month 12. Comparison of mean change in PR loss area revealed statistically significantly less growth in the AM group at month 2, 6 and 12 compared to SM (-41μm ± 219 vs. 77μm ± 126, p=0.0004; -5μm ± 221 vs. 156μm ± 139, p<0.0001; 106μm ± 400 vs. 283μm ± 226 p=0.0014). PR thinning was significantly reduced under monthly treatment compared to sham within the GA junctional zone as well as throughout the 20 degrees area. A trend towards greater inhibition of PR loss compared to RPE loss was observed under therapy.

CONCLUSIONS

Distinct and reliable quantification of PR loss using deep learning-based algorithms offers an essential tool to evaluate therapeutic efficacy in slowing disease progression. PR loss and thinning are reduced by intravitreal complement C3 inhibition. Automated quantification of PR loss/maintenance based on OCT images is an ideal approach to reliably monitor disease activity and therapeutic efficacy in GA management in clinical routine and regulatory trials.

An Update on the Hemodynamic Model of Age-Related Macular Degeneration

PURPOSE

To provide an update on the hemodynamic model of age-related macular degeneration (AMD).

DESIGN

Evidence-based perspective.

METHODS

Review of the literature and experience of the authors.

RESULTS

Choroidal hemodynamics are not the primary cause of AMD as proposed by Ephraim Friedman in 1997. However, evidence is accumulating to suggest that choroidal perfusion is an important environmental influence that contributes to our understanding of disease progression in this complex genetic disorder. Although early and intermediate AMD seem to be influenced to a large extent by the underlying genetics, the asymmetry of disease progression to the later stages of AMD cannot be explained by genetics alone. The progression of disease and the asymmetry of this progression seem to correlate with abnormalities in choroidal perfusion that can be documented by optical coherence tomography. These perfusion abnormalities in the setting of a thickened Bruch's membrane are thought to exacerbate the impaired nutritional exchange between the retinal pigment epithelium and the choriocapillaris. We propose that the genetic susceptibility to develop AMD combined with age-related changes in macular choroidal hemodynamics, such as increasing choriocapillaris perfusion deficits and decreasing choroidal vascular densities, play an important role in disease progression and may help to explain the asymmetry between eyes, particularly in the later stages of AMD.

CONCLUSIONS

This updated hemodynamic model of AMD focuses on disease progression and highlights the importance of age-related changes in the choroidal circulation as a major environmental influence on disease severity in eyes that are genetically susceptible to develop AMD.

HYPERREFLECTIVE FOCI AS IMPORTANT PROGNOSTIC INDICATORS OF PROGRESSION OF RETINITIS PIGMENTOSA

PURPOSE

To investigate the presence and clinical relevance of hyperreflective foci (HRFs) in retinitis pigmentosa.

METHODS

Seventy seven retinitis pigmentosa cases were retrospectively reviewed. The 10-mm wide cross-line macular scans in optical coherence tomography were acquired. Hyperreflective foci were classified according to the location in optical coherence tomography: outer layers within the macula (HRF-outer-central), macular border beyond the central 3 mm (HRF-outer-perifoveal), and choroid (HRF-choroidal). The visual acuity at baseline, at 12 months, and other fundus characteristics were collected.

RESULTS

The mean logMAR best-corrected visual acuity decreased from 0.59 ± 0.66 (20/78 in Snellen) to 0.74 ± 0.81 (20/106 in Snellen) in 1 year. Sixty-six (42.9%), 105 (68.2%), and 98 (63.6%) eyes were classified to HRF-outer-central, HRF-outer-perifoveal, and HRF-choroidal group, respectively. Hyperreflective foci were positively correlated with poorer vision, central macular thinning, and ellipsoid zone disruption (all P < 0.001). Worse vision was associated with older age, macular involvement, and the coexistence of two or three HRF groups (P = 0.014, 0.047, 0.019, <0.001, respectively). Hyperreflective foci developed more frequently in patients with thick choroid than in those with thin choroid. The coexistence of three HRF groups was correlated with quicker visual deterioration (P = 0.034).

CONCLUSION

Hyperreflective foci are common in retinitis pigmentosa and can be a negative prognostic indicator of macular thickness and visual preservation. Thick choroid was associated with all groups of HRFs, especially HRF-choroidal.

Quantitative assessment of retinal fluid in neovascular age-related macular degeneration under anti-VEGF therapy

The retinal world has been revolutionized by optical coherence tomography (OCT) and anti-vascular endothelial growth factor (VEGF) therapy. The numbers of intravitreal injections are on a constant rise and management in neovascular age-related macular degeneration (nAMD) is mainly driven by the qualitative assessment of macular fluid as detected on OCT scans. The presence of macular fluid, particularly subretinal fluid (SRF) and intraretinal fluid (IRF), has been used to trigger re-treatments in clinical trials and the real world. However, large discrepancies can be found between the evaluations of different readers or experts and especially small amounts of macular fluid might be missed during this process. Pixel-wise detection of macular fluid uses an entire OCT volume to calculate exact volumes of retinal fluid. While manual annotations of such pixel-wise fluid detection are unfeasible in a clinical setting, artificial intelligence (AI) is able to overcome this hurdle by providing real-time results of macular fluid in different retinal compartments. Quantitative fluid assessments have been used for various post hoc analyses of randomized controlled trials, providing novel insights into anti-VEGF treatment regimens. Nonetheless, the application of AI-algorithms in a prospective patient care setting is still limited. In this review, we discuss the use of quantitative fluid assessment in nAMD during anti-VEGF therapy and provide an outlook to novel forms of patient care with the support of AI quantifications.

Risk Classification for Progression to Subfoveal Geographic Atrophy in Dry Age-Related Macular Degeneration Using Machine Learning-Enabled Outer Retinal Feature Extraction

BACKGROUND AND OBJECTIVE

To evaluate the utility of spectral-domain optical coherence tomography biomarkers to predict the development of subfoveal geographic atrophy (sfGA).

PATIENTS AND METHODS

This was a retrospective cohort analysis including 137 individuals with dry age-related macular degeneration without sfGA with 5 years of follow-up. Multiple spectral-domain optical coherence tomography quantitative metrics were generated, including ellipsoid zone (EZ) integrity and subretinal pigment epithelium (sub-RPE) compartment features.

RESULTS

Reduced mean EZ-RPE central subfield thickness and increased sub-RPE compartment thickness were significantly different between sfGA convertors and nonconvertors at baseline in both 2-year and 5-year sfGA risk assessment. Longitudinal change assessment showed a significantly higher degradation of EZ integrity in sfGA convertors. The predictive performance of a machine learning classification model based on 5-year and 2-year risk conversion to sfGA demonstrated an area under the receiver operating characteristic curve of 0.92 ± 0.06 and 0.96 ± 0.04, respectively.

CONCLUSIONS

Quantitative outer retinal and sub-RPE feature assessment using a machine learning-enabled retinal segmentation platform provides multiple parameters that are associated with progression to sfGA. [Ophthalmic Surg Lasers Imaging. 2022;53:31-39.].

Significance of Hyperreflective Foci as an Optical Coherence Tomography Biomarker in Retinal Diseases: Characterization and Clinical Implications

Hyperreflective foci (HRF) is a term coined to depict hyperreflective dots or roundish lesions within retinal layers visualized through optical coherence tomography (OCT). Histopathological correlates of HRF are not univocal, spacing from migrating retinal pigment epithelium cells, lipid-laden macrophages, microglial cells, and extravasated proteinaceous or lipid material. Despite this, HRF can be considered OCT biomarkers for disease progression, treatment response, and prognosis in several retinal diseases, including diabetic macular edema, age-related macular degeneration (AMD), retinal vascular occlusions, and inherited retinal dystrophies. The structural features and topographic location of HRF guide the interpretation of their significance in different pathological conditions. The presence of HRF less than 30 μm with reflectivity comparable to the retinal nerve fiber layer in the absence of posterior shadowing in diabetic macular edema indicates an inflammatory phenotype with a better response to steroidal treatment. In AMD, HRF overlying drusen are associated with the development of macular neovascularization, while parafoveal drusen and HRF predispose to macular atrophy. Thus, HRF can be considered a key biomarker in several common retinal diseases. Their recognition and critical interpretation via multimodal imaging are vital to support clinical strategies and management.

Personalized Atrophy Risk Mapping in Age-Related Macular Degeneration

Purpose

To develop and validate an automatic retinal pigment epithelial and outer retinal atrophy (RORA) progression prediction model for nonexudative age-related macular degeneration (AMD) cases in optical coherence tomography (OCT) scans.

Methods

Longitudinal OCT data from 129 eyes/119 patients with RORA was collected and separated into training and testing groups. RORA was automatically segmented in all scans and additionally manually annotated in the test scans. OCT-based features such as layers thicknesses, mean reflectivity, and a drusen height map served as an input to the deep neural network. Based on the baseline OCT scan or the previous visit OCT, en face RORA predictions were calculated for future patient visits. The performance was quantified over time with the means of Dice scores and square root area errors.

Results

The average Dice score for segmentations at baseline was 0.85. When predicting progression from baseline OCTs, the Dice scores ranged from 0.73 to 0.80 for total RORA area and from 0.46 to 0.72 for RORA growth region. The square root area error ranged from 0.13 mm to 0.33 mm. By providing continuous time output, the model enabled creation of a patient-specific atrophy risk map.

Conclusions

We developed a machine learning method for RORA progression prediction, which provides continuous-time output. It was used to compute atrophy risk maps, which indicate time-to-RORA-conversion, a novel and clinically relevant way of representing disease progression.

Translational Relevance

Application of recent advances in artificial intelligence to predict patient-specific progression of atrophic AMD.

Local Progression Kinetics of Geographic Atrophy Depends Upon the Border Location

Purpose

To assess the influence of lesion morphology and location on geographic atrophy (GA) growth rate.

Methods

We manually delineated GA on color fundus photographs of 237 eyes in the Age-Related Eye Disease Study. We calculated local border expansion rate (BER) as the linear distance that a point on the GA border traveled over 1 year based on a Euclidean distance map. Eye-specific BER was defined as the mean local BER of all points on the GA border in an eye. The percentage area affected by GA was defined as the GA area divided by the total retinal area in the region.

Results

GA enlarged 1.51 ± 1.96 mm2 in area and 0.13 ± 0.11 mm in distance over 1 year. The GA area growth rate (mm2/y) was associated with the baseline GA area (P < 0.001), perimeter (P < 0.001), lesion number (P < 0.001), and circularity index (P < 0.001); in contrast, eye-specific BER (mm/y) was not significantly associated with any of these factors. As the retinal eccentricity increased from 0 to 3.5 mm, the local BER increased from 0.10 to 0.24 mm/y (P < 0.001); in contrast, the percentage of area affected by GA decreased from 49.3% to 2.3%.

Conclusions

Using distance-based measurements allows GA progression evaluation without significant confounding effects from baseline GA morphology. Local GA progression rates increased as a function of retinal eccentricity within the macula which is opposite of the trend for GA distribution, suggesting that GA initiation and enlargement may be mediated by different biological processes.

Prediction of keratoconus progression using deep learning of anterior segment optical coherence tomography maps

Background

To predict keratoconus progression using deep learning of the color-coded maps measured with a swept-source anterior segment optical coherence tomography (As-OCT) device.

Methods

We enrolled 218 keratoconic eyes with and without disease progression. Using deep learning of the 6 color-coded maps (anterior elevation, anterior curvature, posterior elevation, posterior curvature, total refractive power, and pachymetry map) obtained by the As-OCT (CASIA, Tomey), we assessed the accuracy, sensitivity, and specificity of prediction of keratoconus progression in such eyes.

Results

Deep learning of the 6 color-coded maps exhibited an accuracy of 0.794 in discriminating keratoconus with and without progression. For a single map analysis, posterior elevation map (0.798) showed the highest accuracy, followed by anterior curvature map (0.775), posterior corneal curvature map (0.757), anterior elevation map (0.752), total refractive power map (0.729), and pachymetry map (0.720), in distinguishing between progressive and non-progressive keratoconus. The use of the adjusted algorithm by age subgroups improved to an accuracy of 0.849.

Conclusions

Deep learning of the As-OCT color-coded maps effectively discriminates progressive keratoconus from non-progressive keratoconus with an accuracy of approximately 85% using the adjusted age algorithm, indicating that it will become an aid for predicting the progression of the disease, which is clinically beneficial for decision-making of the surgical indication of corneal cross-linking (CXL).

Biomarkers for Nonexudative Age-Related Macular Degeneration and Relevance for Clinical Trials: A Systematic Review

TOPIC

The purpose of the review was to identify structural, functional, blood-based, and other types of biomarkers for early, intermediate, and late nonexudative stages of age-related macular degeneration (AMD) and summarize the relevant data for proof-of-concept clinical trials.

CLINICAL RELEVANCE

AMD is a leading cause of blindness in the aging population, yet no treatments exist for its most common nonexudative form. There are limited data on the diagnosis and progression of nonexudative AMD compared to neovascular AMD. Our objective was to provide a comprehensive, systematic review of recently published biomarkers (molecular, structural, and functional) for early AMD, intermediate AMD, and geographic atrophy and to evaluate the relevance of these biomarkers for use in future clinical trials.

METHODS

A literature search of PubMed, ScienceDirect, EMBASE, and Web of Science from January 1, 1996 to November 30, 2020 and a patent search were conducted. Search terms included "early AMD," "dry AMD," "intermediate AMD," "biomarkers for nonexudative AMD," "fundus autofluorescence patterns," "color fundus photography," "dark adaptation," and "microperimetry." Articles were assessed for bias and quality with the Mixed-Methods Appraisal Tool. A total of 94 articles were included (61,842 individuals).

RESULTS

Spectral-domain optical coherence tomography was superior at highlighting detailed structural changes in earlier stages of AMD. Fundus autofluorescence patterns were found to be most important in estimating progression of geographic atrophy. Delayed rod intercept time on dark adaptation was the most widely recommended surrogate functional endpoint for early AMD, while retinal sensitivity on microperimetry was most relevant for intermediate AMD. Combinational studies accounting for various patient characteristics and machine/deep-learning approaches were best suited for assessing individualized risk of AMD onset and progression.

CONCLUSION

This systematic review supports the use of structural and functional biomarkers in early AMD and intermediate AMD, which are more reproducible and less invasive than the other classes of biomarkers described. The use of deep learning and combinational algorithms will gain increasing importance in future clinical trials of nonexudative AMD.

Imaging and artificial intelligence for progression of age-related macular degeneration

Age-related macular degeneration (AMD) is a leading cause of severe vision loss. With our aging population, it may affect 288 million people globally by the year 2040. AMD progresses from an early and intermediate dry form to an advanced one, which manifests as choroidal neovascularization and geographic atrophy. Conversion to AMD-related exudation is known as progression to neovascular AMD, and presence of geographic atrophy is known as progression to advanced dry AMD. AMD progression predictions could enable timely monitoring, earlier detection and treatment, improving vision outcomes. Machine learning approaches, a subset of artificial intelligence applications, applied on imaging data are showing promising results in predicting progression. Extracted biomarkers, specifically from optical coherence tomography scans, are informative in predicting progression events. The purpose of this mini review is to provide an overview about current machine learning applications in artificial intelligence for predicting AMD progression, and describe the various methods, data-input types, and imaging modalities used to identify high-risk patients. With advances in computational capabilities, artificial intelligence applications are likely to transform patient care and management in AMD. External validation studies that improve generalizability to populations and devices, as well as evaluating systems in real-world clinical settings are needed to improve the clinical translations of artificial intelligence AMD applications.

Deep Learning Applied to Automated Segmentation of Geographic Atrophy in Fundus Autofluorescence Images

Purpose

This study describes the development of a deep learning algorithm based on the U-Net architecture for automated segmentation of geographic atrophy (GA) lesions in fundus autofluorescence (FAF) images.

Methods

Image preprocessing and normalization by modified adaptive histogram equalization were used for image standardization to improve effectiveness of deep learning. A U-Net-based deep learning algorithm was developed and trained and tested by fivefold cross-validation using FAF images from clinical datasets. The following metrics were used for evaluating the performance for lesion segmentation in GA: dice similarity coefficient (DSC), DSC loss, sensitivity, specificity, mean absolute error (MAE), accuracy, recall, and precision.

Results

In total, 702 FAF images from 51 patients were analyzed. After fivefold cross-validation for lesion segmentation, the average training and validation scores were found for the most important metric, DSC (0.9874 and 0.9779), for accuracy (0.9912 and 0.9815), for sensitivity (0.9955 and 0.9928), and for specificity (0.8686 and 0.7261). Scores for testing were all similar to the validation scores. The algorithm segmented GA lesions six times more quickly than human performance.

Conclusions

The deep learning algorithm can be implemented using clinical data with a very high level of performance for lesion segmentation. Automation of diagnostics for GA assessment has the potential to provide savings with respect to patient visit duration, operational cost and measurement reliability in routine GA assessments.

Translational Relevance

A deep learning algorithm based on the U-Net architecture and image preprocessing appears to be suitable for automated segmentation of GA lesions on clinical data, producing fast and accurate results.

LONGITUDINAL CHANGES IN QUANTITATIVE AUTOFLUORESCENCE DURING PROGRESSION FROM INTERMEDIATE TO LATE AGE-RELATED MACULAR DEGENERATION

PURPOSE

To prospectively investigate the development of quantitative autofluorescence (qAF) during progression from intermediate to late age-related macular degeneration (AMD).

METHODS

Quantitative autofluorescence images from patients with intermediate AMD were acquired every three months with a Spectralis HRA + OCT (Heidelberg Engineering, Heidelberg, Germany) using a built-in autofluorescence reference. The association between changes in longitudinal qAF and progression toward late AMD was assessed using Cox regression models with time-dependent covariates.

RESULTS

One hundred and twenty-one eyes of 71 patients were included, and 653 qAF images were acquired. Twenty-one eyes of 17 patients converted to late AMD (median follow-up: 21 months; 12 eyes: atrophic AMD; nine eyes: neovascular AMD). The converting patients' mean age was 74.6 ± 4.4 years. Eleven eyes in the converting group (52.4%) were pseudophakic. The presence of an intraocular lens did not affect the qAF regression slopes (P > 0.05). The median change for atrophic AMD was -2.34 qAF units/3 months and 0.78 qAF units/3 months for neovascular AMD. A stronger decline in qAF was significantly associated with an increased risk of developing atrophic AMD (hazard ratio = 1.022, P < 0.001). This association, however, was not present in the group progressing toward neovascular AMD (hazard ratio = 1.001, P = 0.875).

CONCLUSION

The qAF signal declines with progression to atrophy, contrary to developing neovascularization. Quantitative autofluorescence may allow identification of patients at risk of progressing to late AMD and benefits individualized patient care in intermediate AMD.

Automated Quantitative Assessment of Retinal Fluid Volumes as Important Biomarkers in Neovascular Age-Related Macular Degeneration

PURPOSE

To evaluate retinal fluid volume data extracted from optical coherence tomography (OCT) scans by artificial intelligence algorithms in the treatment of neovascular age-related macular degeneration (NV-AMD).

DESIGN

Perspective.

METHODS

A review was performed of retinal image repository datasets from diverse clinical settings.

SETTINGS

Clinical trial (HARBOR) and trial follow-on (Age-Related Eye Disease Study 2 10-year Follow-On); real-world (Belfast and Tel-Aviv tertiary centers).

PATIENTS

24,362 scans of 1,095 eyes (HARBOR); 4,673 of 880 (Belfast); 1,470 of 132 (Tel-Aviv); 511 of 511 (Age-Related Eye Disease Study 2 10-year Follow-On). ObservationProcedures: Vienna Fluid Monitor or Notal OCT Analyzer applied to macular cube scans. OutcomeMeasures: Intraretinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED) volumes.

RESULTS

The fluid volumes measured in neovascular AMD were expressed efficiently in nanoliters. Large ranges that differed by population were observed at the treatment-naïve stage: 0-3,435 nL (IRF), 0-5,018 nL (SRF), and 0-10,022 nL (PED). Mean volumes decreased rapidly and consistently with anti-vascular endothelial growth factor therapy. During maintenance therapy, mean IRF volumes were highest in Tel-Aviv (100 nL), lower in Belfast and HARBOR-Pro Re Nata, and lowest in HARBOR-monthly (21 nL). Mean SRF volumes were low in all: 30 nL (HARBOR-monthly) and 48-49 nL (others).

CONCLUSIONS

Quantitative measures of IRF, SRF, and PED are important biomarkers in NV-AMD. Accurate volumes can be extracted efficiently from OCT scans by artificial intelligence algorithms to guide the treatment of exudative macular diseases. Automated fluid monitoring identifies fluid characteristics in different NV-AMD populations at baseline and during follow-up. For consistency between studies, we propose the nanoliter as a convenient unit. We explore the advantages of using these quantitative metrics in clinical practice and research.

Reliability of Retinal Pathology Quantification in Age-Related Macular Degeneration: Implications for Clinical Trials and Machine Learning Applications

Purpose

To investigate the interreader agreement for grading of retinal alterations in age-related macular degeneration (AMD) using a reading center setting.

Methods

In this cross-sectional case series, spectral-domain optical coherence tomography (OCT; Topcon 3D OCT, Tokyo, Japan) scans of 112 eyes of 112 patients with neovascular AMD (56 treatment naive, 56 after three anti-vascular endothelial growth factor injections) were analyzed by four independent readers. Imaging features specific for AMD were annotated using a novel custom-built annotation platform. Dice score, Bland-Altman plots, coefficients of repeatability, coefficients of variation, and intraclass correlation coefficients were assessed.

Results

Loss of ellipsoid zone, pigment epithelium detachment, subretinal fluid, and drusen were the most abundant features in our cohort. Subretinal fluid, intraretinal fluid, hypertransmission, descent of the outer plexiform layer, and pigment epithelium detachment showed highest interreader agreement, while detection and measures of loss of ellipsoid zone and retinal pigment epithelium were more variable. The agreement on the size and location of the respective annotation was more consistent throughout all features.

Conclusions

The interreader agreement depended on the respective OCT-based feature. A selection of reliable features might provide suitable surrogate markers for disease progression and possible treatment effects focusing on different disease stages.

Translational Relevance

This might give opportunities for a more time- and cost-effective patient assessment and improved decision making as well as have implications for clinical trials and training machine learning algorithms.

Artificial Intelligence Algorithms for Analysis of Geographic Atrophy: A Review and Evaluation

Purpose

The purpose of this study was to summarize and evaluate artificial intelligence (AI) algorithms used in geographic atrophy (GA) diagnostic processes (e.g. isolating lesions or disease progression).

Methods

The search strategy and selection of publications were both conducted in accordance with the Preferred of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed and Web of Science were used to extract literary data. The algorithms were summarized by objective, performance, and scope of coverage of GA diagnosis (e.g. lesion automation and GA progression).

Results

Twenty-seven studies were identified for this review. A total of 18 publications focused on lesion segmentation only, 2 were designed to detect and classify GA, 2 were designed to predict future overall GA progression, 3 focused on prediction of future spatial GA progression, and 2 focused on prediction of visual function in GA. GA-related algorithms reported sensitivities from 0.47 to 0.98, specificities from 0.73 to 0.99, accuracies from 0.42 to 0.995, and Dice coefficients from 0.66 to 0.89.

Conclusions

Current GA-AI publications have a predominant focus on lesion segmentation and a minor focus on classification and progression analysis. AI could be applied to other facets of GA diagnoses, such as understanding the role of hyperfluorescent areas in GA. Using AI for GA has several advantages, including improved diagnostic accuracy and faster processing speeds.

Translational Relevance

AI can be used to quantify GA lesions and therefore allows one to impute visual function and quality-of-life. However, there is a need for the development of reliable and objective models and software to predict the rate of GA progression and to quantify improvements due to interventions.

Developing a potential retinal OCT biomarker for local growth of geographic atrophy

Geographic atrophy (GA), the advanced stage of age-related macular degeneration, is a leading cause of blindness. GA lesions are characterized by anisotropic growth and the ability to predict growth patterns would be valuable in assessing potential therapeutics. In this study, we propose an OCT-based marker of local GA growth rate based on an axial projection of the OCT volume in the Henle fiber layer (HFL) and outer nuclear layer (ONL). We analyze the association between our proposed metric and local GA growth rates in a small longitudinal cohort of patients with AMD. These methods can potentially be used to identify risk markers, stratify patients, or assess response in future therapeutic studies.