SYSTEMATIC CORRELATION OF CENTRAL SUBFIELD THICKNESS WITH RETINAL FLUID VOLUMES QUANTIFIED BY DEEP LEARNING IN THE MAJOR EXUDATIVE MACULAR DISEASES.

PURPOSE: To investigate the correlation of volumetric measurements of intraretinal (IRF) and subretinal fluid obtained by deep learning and central retinal subfield thickness (CSFT) based on optical coherence tomography in retinal vein occlusion, diabetic macular edema, and neovascular age-related macular degeneration. METHODS: A previously validated deep learning-based approach was used for automated segmentation of IRF and subretinal fluid in spectral domain optical coherence tomography images. Optical coherence tomography volumes of 2.433 patients obtained from multicenter studies were analyzed. Fluid volumes were measured at baseline and under antivascular endothelial growth factor therapy in the central 1, 3, and 6 mm. RESULTS: Patients with neovascular age-related macular degeneration generally demonstrated the weakest association between CSFT and fluid volume measurements in the central 1 mm (0.107-0.569). In patients with diabetic macular edema, IRF correlated moderately with CSFT (0.668-0.797). In patients with retinal vein occlusion, IRF volumes showed a moderate correlation with CSFT (0.603-0.704). CONCLUSION: The correlation of CSFT and fluid volumes depends on the underlying pathology. Although the amount of central IRF seems to partly drive CSFT in diabetic macular edema and retinal vein occlusion, it has only a limited impact on patients with neovascular age-related macular degeneration. Our findings do not support the use of CSFT as a primary or secondary outcome measure for the quantification of exudative activity or treatment guidance.

VALIDATION OF AN AUTOMATED FLUID ALGORITHM ON REAL-WORLD DATA OF NEOVASCULAR AGE-RELATED MACULAR DEGENERATION OVER FIVE YEARS.

BACKGROUND/PURPOSE: To apply an automated deep learning automated fluid algorithm on data from real-world management of patients with neovascular age-related macular degeneration for quantification of intraretinal/subretinal fluid volumes in optical coherence tomography images. METHODS: Data from the Vienna Imaging Biomarker Eye Study (VIBES, 2007-2018) were analyzed. Databases were filtered for treatment-naive neovascular age-related macular degeneration with a baseline optical coherence tomography and at least one follow-up and 1,127 eyes included. Visual acuity and optical coherence tomography at baseline, Months 1 to 3/Years 1 to 5, age, sex, and treatment number were included. Artificial intelligence and certified manual grading were compared in a subanalysis of 20%. Main outcome measures were fluid volumes. RESULTS: Intraretinal/subretinal fluid volumes were maximum at baseline (intraretinal fluid: 21.5/76.6/107.1 nL; subretinal fluid 13.7/86/262.5 nL in the 1/3/6-mm area). Intraretinal fluid decreased to 5 nL at M1-M3 (1-mm) and increased to 11 nL (Y1) and 16 nL (Y5). Subretinal fluid decreased to a mean of 4 nL at M1-M3 (1-mm) and remained stable below 7 nL until Y5. Intraretinal fluid was the only variable that reflected VA change over time. Comparison with human expert readings confirmed an area under the curve of >0.9. CONCLUSION: The Vienna Fluid Monitor can precisely quantify fluid volumes in optical coherence tomography images from clinical routine over 5 years. Automated tools will introduce precision medicine based on fluid guidance into real-world management of exudative disease, improving clinical outcomes while saving resources.

IMPACT OF RESIDUAL SUBRETINAL FLUID VOLUMES ON TREATMENT OUTCOMES IN A SUBRETINAL FLUID-TOLERANT TREAT-AND-EXTEND REGIMEN.

PURPOSE: To investigate associations between residual subretinal fluid (rSRF) volumes, quantified using artificial intelligence and treatment outcomes in a subretinal fluid (SRF)-tolerant treat-and-extend (T&E) regimen in neovascular age-related macular degeneration. METHODS: Patients enrolled in the prospective, multicenter FLUID study randomized in an SRF-tolerant T&E regimen were examined by spectral-domain optical coherence tomography and tested for best-corrected visual acuity (BCVA). Intraretinal fluid and SRF volumes were quantified using artificial intelligence tools. In total, 375 visits of 98 patients were divided into subgroups: extended intervals despite rSRF and extended intervals without fluid. Associations between BCVA change, SRF volume, subgroups, and treatment intervals were estimated using linear mixed models. RESULTS: In extended intervals despite rSRF, increased SRF was associated with reduced BCVA at the next visit in the central 1 mm (-0.138 letters per nL; P = 0.014) and 6 mm (-0.024 letters per nL; P = 0.049). A negative association between increased interval and BCVA change was found for rSRF in 1 mm and 6 mm (-0.250 and -0.233 letter per week interval, respectively; both P < 0.001). Extended intervals despite rSRF had significantly higher SRF volumes in the central 6 mm at the following visit (P = 0.002). CONCLUSION: Artificial intelligence-based analysis of extended visits despite rSRF demonstrated increasing SRF volumes associated with BCVA loss at the consecutive visit. This negative association contributes to the understanding of rSRF volumes on treatment outcomes in neovascular age-related macular degeneration.

ANALYSIS OF FLUID VOLUME AND ITS IMPACT ON VISUAL ACUITY IN THE FLUID STUDY AS QUANTIFIED WITH DEEP LEARNING.

PURPOSE: To investigate quantitative differences in fluid volumes between subretinal fluid (SRF)-tolerant and SRF-intolerant treat-and-extend regimens for neovascular age-related macular degeneration and analyze the association with best-corrected visual acuity. METHODS: Macular fluid (SRF and intraretinal fluid) was quantified on optical coherence tomography volumetric scans using a trained and validated deep learning algorithm. Fluid volumes and complete resolution was automatically assessed throughout the study. The impact of fluid location and volumes on best-corrected visual acuity was computed using mixed-effects regression models. RESULTS: Baseline fluid quantifications for 348 eyes from 348 patients were balanced (all P > 0.05). No quantitative differences in SRF/intraretinal fluid between the treatment arms was found at any study-specific time point (all P > 0.05). Compared with qualitative assessment, the proportion of eyes without SRF/intraretinal fluid did not differ between the groups at any time point (all P > 0.05). Intraretinal fluid in the central 1 mm and SRF in the 1-mm to 6-mm macular area were negatively associated with best-corrected visual acuity (-2.8 letters/100 nL intraretinal fluid, P = 0.007 and -0.20 letters/100 nL SRF, P = 0.005, respectively). CONCLUSION: Automated fluid quantification using artificial intelligence allows objective and precise assessment of macular fluid volume and location. Precise determination of fluid parameters will help improve therapeutic efficacy of treatment in neovascular age-related macular degeneration.

Application of Automated Quantification of Fluid Volumes to Anti-VEGF Therapy of Neovascular Age-Related Macular Degeneration.

PURPOSE: Anti-vascular endothelial growth factor (VEGF) treatment of neovascular age-related macular degeneration (AMD) is a highly effective advance in the retinal armentarium. OCT offering 3-dimensional imaging of the retina is widely used to guide treatment. Although poor outcomes reported from clinical practice are multifactorial, availability of reliable, reproducible, and quantitative evaluation tools to accurately measure the fluid response, that is, a "VEGF meter," may be a better means of monitoring and treating than the current purely qualitative evaluation used in clinical practice. DESIGN: Post hoc analysis of a phase III, randomized, multicenter study. PARTICIPANTS: Study eyes of 1095 treatment-naive subjects receiving pro re nata (PRN) or monthly ranibizumab therapy according to protocol-specified criteria in the HARBOR study. METHODS: A deep learning method for localization and quantification of fluid in all retinal compartments was applied for automated segmentation of fluid with every voxel classified by a convolutional neural network (CNN). Three-dimensional volumes (nanoliters) for intraretinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED) were determined in 24 362 volume scans obtained from 1095 patients treated over 24 months in a phase III clinical trial with randomization to 2 drug dosages (0.5 mg and 2.0 mg ranibizumab) and 2 regimens (monthly and PRN). A multivariable mixed-effects regression model was used to test for differences in fluid between the arms and for fluid/function correlation. MAIN OUTCOME MEASURES: Fluid volume in nanoliters, structure-function as Pearson's correlation coefficient, and as a coefficient of determination (R2). RESULTS: Fluid volumes were quantified in all visits of all patients. Automated segmentation demonstrated characteristic response patterns for each fluid compartment individually: Intraretinal fluid showed the greatest and most rapid resolution, followed by SRF and PED the least. The loading dose treatment achieved resolution of all fluid types close to the lowest levels attainable. Dosage and regimen parameters correlated directly with resulting fluid volumes. Fluid/function correlation showed a volume-dependent negative impact of IRF on vision and weak positive prognostic effect of SRF. CONCLUSIONS: Automated quantification of the fluid response may improve therapeutic management of neovascular AMD, avoid discrepancies between clinicians/investigators, and establish structure/function correlations.

MORPHOLOGICAL AND FUNCTIONAL CHARACTERISTICS AT THE ONSET OF EXUDATIVE CONVERSION IN AGE-RELATED MACULAR DEGENERATION.

PURPOSE: To characterize retinal morphology differences among different types of choroidal neovascularization and visual function changes at the onset of exudative age-related macular degeneration. METHODS: In a post hoc analysis of a prospective clinical study, 1,097 fellow eyes from subjects with choroidal neovascularization in the study eye enrolled in the HARBOR trial were evaluated. The onset of exudation was diagnosed on monthly optical coherence tomography by two masked graders. At conversion as well as 1 month earlier, pigment epithelial detachment, intraretinal cystoid fluid, subretinal fluid, subretinal hyperreflective material, as well as ellipsoid zone and external limiting membrane loss were quantitatively analyzed. Hyperreflective foci, retinal pigment epithelial defects, haze and vitreoretinal interface status were evaluated qualitatively. Main outcome measures included visual acuity and rates of morphologic features at conversion and 1 month earlier. RESULTS: New-onset exudation was detected in 92 eyes. One month before conversion, hyperreflective foci, pigment epithelial detachment, retinal pigment epithelial defects, and haze were present in the majority of eyes. At the onset of exudation, the volumes of intraretinal cystoid fluid, subretinal fluid, subretinal hyperreflective material and pigment epithelial detachment, and the areas of external limiting membrane and ellipsoid zone loss significantly increased. The mean vision loss was -2.2 letters. Pathognomonic patterns of the different choroidal neovascularization types were already apparent 1 month before conversion. CONCLUSION: Characteristic choroidal neovascularization-associated morphological changes are preceding disease conversion, while vision loss at the onset of exudation is minimal. Individual lesion types are related to specific changes in optical coherence tomography morphology already before the time of conversion. Our findings may help to elucidate the pathophysiology of neovascular age-related macular degeneration and support the diagnosis of imminent disease conversion.

Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning.

PURPOSE: Development and validation of a fully automated method to detect and quantify macular fluid in conventional OCT images. DESIGN: Development of a diagnostic modality. PARTICIPANTS: The clinical dataset for fluid detection consisted of 1200 OCT volumes of patients with neovascular age-related macular degeneration (AMD, n = 400), diabetic macular edema (DME, n = 400), or retinal vein occlusion (RVO, n = 400) acquired with Zeiss Cirrus (Carl Zeiss Meditec, Dublin, CA) (n = 600) or Heidelberg Spectralis (Heidelberg Engineering, Heidelberg, Germany) (n = 600) OCT devices. METHODS: A method based on deep learning to automatically detect and quantify intraretinal cystoid fluid (IRC) and subretinal fluid (SRF) was developed. The performance of the algorithm in accurately identifying fluid localization and extent was evaluated against a manual consensus reading of 2 masked reading center graders. MAIN OUTCOME MEASURES: Performance of a fully automated method to accurately detect, differentiate, and quantify intraretinal and SRF using area under the receiver operating characteristics curves, precision, and recall. RESULTS: The newly designed, fully automated diagnostic method based on deep learning achieved optimal accuracy for the detection and quantification of IRC for all 3 macular pathologies with a mean accuracy (AUC) of 0.94 (range, 0.91-0.97), a mean precision of 0.91, and a mean recall of 0.84. The detection and measurement of SRF were also highly accurate with an AUC of 0.92 (range, 0.86-0.98), a mean precision of 0.61, and a mean recall of 0.81, with superior performance in neovascular AMD and RVO compared with DME, which was represented rarely in the population studied. High linear correlation was confirmed between automated and manual fluid localization and quantification, yielding an average Pearson's correlation coefficient of 0.90 for IRC and of 0.96 for SRF. CONCLUSIONS: Deep learning in retinal image analysis achieves excellent accuracy for the differential detection of retinal fluid types across the most prevalent exudative macular diseases and OCT devices. Furthermore, quantification of fluid achieves a high level of concordance with manual expert assessment. Fully automated analysis of retinal OCT images from clinical routine provides a promising horizon in improving accuracy and reliability of retinal diagnosis for research and clinical practice in ophthalmology.

Learning Spatio-Temporal Model of Disease Progression With NeuralODEs From Longitudinal Volumetric Data.

Robust forecasting of the future anatomical changes inflicted by an ongoing disease is an extremely challenging task that is out of grasp even for experienced healthcare professionals. Such a capability, however, is of great importance since it can improve patient management by providing information on the speed of disease progression already at the admission stage, or it can enrich the clinical trials with fast progressors and avoid the need for control arms by the means of digital twins. In this work, we develop a deep learning method that models the evolution of age-related disease by processing a single medical scan and providing a segmentation of the target anatomy at a requested future point in time. Our method represents a time-invariant physical process and solves a large-scale problem of modeling temporal pixel-level changes utilizing NeuralODEs. In addition, we demonstrate the approaches to incorporate the prior domain-specific constraints into our method and define temporal Dice loss for learning temporal objectives. To evaluate the applicability of our approach across different age-related diseases and imaging modalities, we developed and tested the proposed method on the datasets with 967 retinal OCT volumes of 100 patients with Geographic Atrophy and 2823 brain MRI volumes of 633 patients with Alzheimer's Disease. For Geographic Atrophy, the proposed method outperformed the related baseline models in the atrophy growth prediction. For Alzheimer's Disease, the proposed method demonstrated remarkable performance in predicting the brain ventricle changes induced by the disease, achieving the state-of-the-art result on TADPOLE cross-sectional prediction challenge dataset.

Deep Multimodal Fusion of Data With Heterogeneous Dimensionality via Projective Networks.

The use of multimodal imaging has led to significant improvements in the diagnosis and treatment of many diseases. Similar to clinical practice, some works have demonstrated the benefits of multimodal fusion for automatic segmentation and classification using deep learning-based methods. However, current segmentation methods are limited to fusion of modalities with the same dimensionality (e.g., 3D + 3D, 2D + 2D), which is not always possible, and the fusion strategies implemented by classification methods are incompatible with localization tasks. In this work, we propose a novel deep learning-based framework for the fusion of multimodal data with heterogeneous dimensionality (e.g., 3D + 2D) that is compatible with localization tasks. The proposed framework extracts the features of the different modalities and projects them into the common feature subspace. The projected features are then fused and further processed to obtain the final prediction. The framework was validated on the following tasks: segmentation of geographic atrophy (GA), a late-stage manifestation of age-related macular degeneration, and segmentation of retinal blood vessels (RBV) in multimodal retinal imaging. Our results show that the proposed method outperforms the state-of-the-art monomodal methods on GA and RBV segmentation by up to 3.10% and 4.64% Dice, respectively.

Analysing early changes of photoreceptor layer thickness following surgery in eyes with epiretinal membranes.

BACKGROUND/OBJECTIVES: To analyse short-term changes of mean photoreceptor thickness (PRT) on the ETDRS-grid after vitrectomy and membrane peeling in patients with epiretinal membrane (ERM). SUBJECTS/METHODS: Forty-eight patients with idiopathic ERM were included in this prospective study. Study examinations comprised best-corrected visual acuity (BCVA) and optical coherence tomography (OCT) before surgery, 1 week (W1), 1 month (M1) and 3 months (M3) after surgery. Mean PRT was assessed using an automated algorithm and correlated with BCVA and central retinal thickness (CRT). RESULTS: Regarding PRT changes of the study eye in comparison to baseline values, a significant decrease at W1 in the 1 mm, 3 mm and 6 mm area (all p-values < 0.001), at M1 (p = 0.009) and M3 (p = 0.019) in the central 1 mm area, a significant increase at M3 in the 6 mm area (p < 0.001), but no significant change at M1 in the 3 mm and 6 mm area and M3 in the 3 mm area (all p-values > 0.05) were observed. BCVA increased significantly from baseline to M3 (0.3LogMAR-0.15LogMAR, Snellen equivalent = 20/40-20/28 respectively; p < 0.001). There was no correlation between baseline PRT and BCVA at any visit after surgery, nor between PRT and BCVA at any visit (all p-values > 0.05). Decrease in PRT in the 1 mm (p < 0.001), 3 mm (p = 0.013) and 6 mm (p = 0.034) area after one week correlated with the increase in CRT (449.9 µm-462.2 µm). CONCLUSIONS: Although the photoreceptor layer is morphologically affected by ERMs and after their surgical removal, it is not correlated to BCVA. Thus, patients with photoreceptor layer alterations due to ERM may still benefit from surgery and achieve good functional rehabilitation thereafter.

An Automated Comparative Analysis of the Exudative Biomarkers in Neovascular Age-Related Macular Degeneration, The RAP Study: Report 6.

PURPOSE: To investigate differences in volume and distribution of the main exudative biomarkers across all types and subtypes of macular neovascularization (MNV) using artificial intelligence (AI). DESIGN: Cross-sectional study. METHODS: An AI-based analysis was conducted on 34,528 OCT B-scans consisting of 281 (250 unifocal, 31 multifocal) MNV3, 55 MNV2, and 121 (30 polypoidal, 91 non-polypoidal) MNV1 treatment-naive eyes. Means (SDs), medians and heat maps of cystic intraretinal fluid (IRF), subretinal fluid (SRF), pigment epithelial detachments (PED), and hyperreflective foci (HRF) volumes, as well as retinal thickness (RT) were compared among MNV types and subtypes. RESULTS: MNV3 had the highest mean IRF with 291 (290) nL, RT with 357 (49) µm, and HRF with 80 (70) nL, P ≤ .05. MNV1 showed the greatest mean SRF with 492 (586) nL, whereas MNV3 exhibited the lowest with 218 (382) nL, P ≤ .05. Heat maps showed IRF confined to the center, whereas SRF was scattered in all types. SRF, HRF, and PED were more distributed in the temporal macular half in MNV3. Means of IRF, HRF, and PED were higher in the multifocal than in the unifocal MNV3 with 416 (309) nL,114 (95) nL, and 810 (850) nL, P ≤ .05. Compared to the non-polypoidal subtype, the polypoidal subtype had greater means of SRF with 695 (718) nL, HRF 69 (63) nL, RT 357 (45) µm, and PED 1115 (1170) nL, P ≤ .05. CONCLUSIONS: This novel quantitative AI analysis shows that SRF is a biomarker of choroidal origin in MNV1, whereas IRF, HRF, and RT are retinal biomarkers in MNV3. Polypoidal MNV1 and multifocal MNV3 present with higher exudation compared to other subtypes.

AI-based support for optical coherence tomography in age-related macular degeneration.

Artificial intelligence (AI) has emerged as a transformative technology across various fields, and its applications in the medical domain, particularly in ophthalmology, has gained significant attention. The vast amount of high-resolution image data, such as optical coherence tomography (OCT) images, has been a driving force behind AI growth in this field. Age-related macular degeneration (AMD) is one of the leading causes for blindness in the world, affecting approximately 196 million people worldwide in 2020. Multimodal imaging has been for a long time the gold standard for diagnosing patients with AMD, however, currently treatment and follow-up in routine disease management are mainly driven by OCT imaging. AI-based algorithms have by their precision, reproducibility and speed, the potential to reliably quantify biomarkers, predict disease progression and assist treatment decisions in clinical routine as well as academic studies. This review paper aims to provide a summary of the current state of AI in AMD, focusing on its applications, challenges, and prospects.

Anomaly guided segmentation: Introducing semantic context for lesion segmentation in retinal OCT using weak context supervision from anomaly detection.

Automated lesion detection in retinal optical coherence tomography (OCT) scans has shown promise for several clinical applications, including diagnosis, monitoring and guidance of treatment decisions. However, segmentation models still struggle to achieve the desired results for some complex lesions or datasets that commonly occur in real-world, e.g. due to variability of lesion phenotypes, image quality or disease appearance. While several techniques have been proposed to improve them, one line of research that has not yet been investigated is the incorporation of additional semantic context through the application of anomaly detection models. In this study we experimentally show that incorporating weak anomaly labels to standard segmentation models consistently improves lesion segmentation results. This can be done relatively easy by detecting anomalies with a separate model and then adding these output masks as an extra class for training the segmentation model. This provides additional semantic context without requiring extra manual labels. We empirically validated this strategy using two in-house and two publicly available retinal OCT datasets for multiple lesion targets, demonstrating the potential of this generic anomaly guided segmentation approach to be used as an extra tool for improving lesion detection models.

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.

Deep Neural Networks for Automated Outer Plexiform Layer Subsidence Detection on Retinal OCT of Patients With Intermediate AMD.

Purpose: The subsidence of the outer plexiform layer (OPL) is an important imaging biomarker on optical coherence tomography (OCT) associated with early outer retinal atrophy and a risk factor for progression to geographic atrophy in patients with intermediate age-related macular degeneration (AMD). Deep neural networks (DNNs) for OCT can support automated detection and localization of this biomarker. Methods: The method predicts potential OPL subsidence locations on retinal OCTs. A detection module (DM) infers bounding boxes around subsidences with a likelihood score, and a classification module (CM) assesses subsidence presence at the B-scan level. Overlapping boxes between B-scans are combined and scored by the product of the DM and CM predictions. The volume-wise score is the maximum prediction across all B-scans. One development and one independent external data set were used with 140 and 26 patients with AMD, respectively. Results: The system detected more than 85% of OPL subsidences with less than one false-positive (FP)/scan. The average area under the curve was 0.94 ± 0.03 for volume-level detection. Similar or better performance was achieved on the independent external data set. Conclusions: DNN systems can efficiently perform automated retinal layer subsidence detection in retinal OCT images. In particular, the proposed DNN system detects OPL subsidence with high sensitivity and a very limited number of FP detections. Translational Relevance: DNNs enable objective identification of early signs associated with high risk of progression to the atrophic late stage of AMD, ideally suited for screening and assessing the efficacy of the interventions aiming to slow disease progression.

Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation.

Pathologic myopia (PM) is a common blinding retinal degeneration suffered by highly myopic population. Early screening of this condition can reduce the damage caused by the associated fundus lesions and therefore prevent vision loss. Automated diagnostic tools based on artificial intelligence methods can benefit this process by aiding clinicians to identify disease signs or to screen mass populations using color fundus photographs as inputs. This paper provides insights about PALM, our open fundus imaging dataset for pathological myopia recognition and anatomical structure annotation. Our databases comprises 1200 images with associated labels for the pathologic myopia category and manual annotations of the optic disc, the position of the fovea and delineations of lesions such as patchy retinal atrophy (including peripapillary atrophy) and retinal detachment. In addition, this paper elaborates on other details such as the labeling process used to construct the database, the quality and characteristics of the samples and provides other relevant usage notes.

Dynamics and patterns of recurrence in neovascular AMD during real-world management using automated fluid monitoring.

In this retrospective longitudinal observational study, data from one site of the Fight Retinal Blindness! Registry (University of Zurich, Switzerland) was used to investigate the quantity and distribution of recurrent fluid in neovascular age-related macular degeneration (nAMD). Study eye eligibility required treatment-naïve nAMD, receiving at least three anti-vascular endothelial growth factor injections, followed by a treatment discontinuation of at least six months and subsequence fluid recurrence. To quantify fluid, a regulatory approved deep learning algorithm (Vienna Fluid Monitor, RetInSight, Vienna, Austria) was used. Fifty-six eyes of 56 patients with a mean age of 76.29 ± 6.58 years at baseline fulfilled the inclusion criteria. From baseline to the end of the first treatment-free interval, SRF volume had decreased significantly (58.0 nl (IQR 10-257 nl) to 8.73 nl (IQR 1-100 nl), p < 0.01). The quantitative increase in IRF volume from baseline to the end of the first treatment-free interval was not statistically significant (1.35 nl (IQR 0-107 nl) to 5.18 nl (IQR 0-24 nl), p = 0.13). PED also did not reach statistical significance (p = 0.71). At the end of the second treatment discontinuation there was quantitatively more IRF (17.3 nl) than SRF (3.74 nl). In conclusion, discontinuation of treatment with anti-VEGF therapy may change the fluid pattern in nAMD.

Advancing the visibility of outer retinal integrity in neovascular age-related macular degeneration with high-resolution OCT.

OBJECTIVE: To compare the visibility and accessibility of the outer retina in neovascular age-related macular degeneration (nAMD) between 2 OCT devices. METHODS: In this prospective, cross-sectional exploratory study, differences in thickness and loss of individual outer retinal layers in eyes with nAMD and in age-matched healthy eyes between a next-level High-Res OCT device and the conventional SPECTRALIS OCT (both Heidelberg Engineering GmbH, Heidelberg, Germany) were analyzed. Eyes with nAMD and at least 250 nL of retinal fluid, quantified by an approved deep-learning algorithm (Fluid Monitor, RetInSight, Vienna, Austria), fulfilled the inclusion criteria. The outer retinal layers were segmented using automated layer segmentation and were corrected manually. Layer loss and thickness were compared between both devices using a linear mixed-effects model and a paired t test. RESULTS: Nineteen eyes of 17 patients with active nAMD and 17 healthy eyes were included. For nAMD eyes, the thickness of the retinal pigment epithelium (RPE) differed significantly between the devices (25.42 µm [95% CI, 14.24-36.61] and 27.31 µm [95% CI, 16.12-38.50] for high-resolution OCT and conventional OCT, respectively; p = 0.033). Furthermore, a significant difference was found in the mean relative external limiting membrane loss (p = 0.021). However, the thickness of photoreceptors, RPE integrity loss, and photoreceptor integrity loss did not differ significantly between devices in the central 3 mm. In healthy eyes, a significant difference in both RPE and photoreceptor thickness between devices was shown (p < 0.001). CONCLUSION: Central RPE thickness was significantly thinner on high-resolution OCT compared with conventional OCT images explained by superior optical separation of the RPE and Bruch's membrane.

Morph-SSL: Self-Supervision with Longitudinal Morphing for Forecasting AMD Progression from OCT Volumes.

The lack of reliable biomarkers makes predicting the conversion from intermediate to neovascular age-related macular degeneration (iAMD, nAMD) a challenging task. We develop a Deep Learning (DL) model to predict the future risk of conversion of an eye from iAMD to nAMD from its current OCT scan. Although eye clinics generate vast amounts of longitudinal OCT scans to monitor AMD progression, only a small subset can be manually labeled for supervised DL. To address this issue, we propose Morph-SSL, a novel Self-supervised Learning (SSL) method for longitudinal data. It uses pairs of unlabelled OCT scans from different visits and involves morphing the scan from the previous visit to the next. The Decoder predicts the transformation for morphing and ensures a smooth feature manifold that can generate intermediate scans between visits through linear interpolation. Next, the Morph-SSL trained features are input to a Classifier which is trained in a supervised manner to model the cumulative probability distribution of the time to conversion with a sigmoidal function. Morph-SSL was trained on unlabelled scans of 399 eyes (3570 visits). The Classifier was evaluated with a five-fold cross-validation on 2418 scans from 343 eyes with clinical labels of the conversion date. The Morph-SSL features achieved an AUC of 0.779 in predicting the conversion to nAMD within the next 6 months, outperforming the same network when trained end-to-end from scratch or pre-trained with popular SSL methods. Automated prediction of the future risk of nAMD onset can enable timely treatment and individualized AMD management.