Deep Learning-Based Detection of Reticular Pseudodrusen in Age-Related Macular Degeneration on Optical Coherence Tomography.

Reticular pseudodrusen (RPD) signify a critical phenotype driving vision loss in age-related macular degeneration (AMD). Their detection is paramount in the clinical management of those with AMD, yet they remain challenging to reliably identify. We thus developed a deep learning (DL) model to segment RPD from 9,800 optical coherence tomography B-scans, and this model produced RPD segmentations that had higher agreement with four retinal specialists (Dice similarity coefficient [DSC]=0·76 [95% confidence interval [CI] 0·71-0·81]) than the agreement amongst the specialists (DSC=0·68, 95% CI=0·63-0·73; p <0·001). In five external test datasets consisting of 1,017 eyes from 812 individuals, the DL model detected RPD with a similar level of performance as two retinal specialists (area-under-the-curve of 0·94 [95% CI=0·92-0·97], 0·95 [95% CI=0·92-0·97] and 0·96 [95% CI=0·94-0·98] respectively; p ≥0·32). This DL model enables the automatic detection and quantification of RPD with expert-level performance, which we have made publicly available.

Cataract Surgery Is Not Associated with Decreased Risk of Retinal Vein Occlusion.

Purpose: To evaluate whether cataract surgery is associated with decreased risks of central retinal vein occlusion (CRVO) or branch retinal vein occlusion (BRVO) development using the American Academy of Ophthalmology Intelligent Research in Sight (IRIS®) Registry. Design: Retrospective database study of the IRIS Registry data. Participants: Patients in the IRIS Registry who underwent cataract surgery and 1:1 matched control participants from the IRIS Registry using a decision tree classifier as a propensity model. Methods: Control and treatment groups initially were selected using Current Procedural Terminology codes for uncomplicated cataract surgery and other straightforward criteria. To accomplish treatment-control matching, a decision tree classifier was trained to classify patients as treatment versus control based on a set of chosen predictors for treatment, where best-corrected visual acuity and age were the most important predictors. Treatment and control participants subsequently were matched using the classifier, the visit dates, and the identifications of the practice. Cox regression was performed on the matched groups to measure the hazard ratio (HR) of retinal vein occlusion development adjusted for age, sex, race, primary insurance type, and previous diagnosis of diabetic retinopathy (DR), glaucoma, and narrow angles. Main Outcome Measure: The HR of retinal vein occlusion developing in patients who underwent cataract surgery compared with matched control participants. Results: The HRs for CRVO and BRVO developing in patients who underwent cataract surgery compared with matched control participants who did not during the first year after either cataract surgery or baseline visit were 1.26 [95% confidence interval [CI], 1.16-1.38; P < 0.001] and 1.27 [95% CI, 1.19-1.36; P < 0.001], respectively, after controlling for age, sex, race, insurance, and history of DR, glaucoma, and narrow angles. Diabetic retinopathy was the strongest predictor associated with CRVO (2.79 [95% CI, 2.43-3.20; P < 0.001]) and BRVO (2.35 [95% CI, 2.09-2.64; P < 0.001]) development after cataract surgery. Conclusions: Cataract surgery is associated with a small increase in risk of retinal vein occlusions within the first year; however, the incidence is low and likely not clinically significant.

Exploring a Structural Basis for Delayed Rod-Mediated Dark Adaptation in Age-Related Macular Degeneration Via Deep Learning.

Purpose: Delayed rod-mediated dark adaptation (RMDA) is a functional biomarker for incipient age-related macular degeneration (AMD). We used anatomically restricted spectral domain optical coherence tomography (SD-OCT) imaging data to localize de novo imaging features associated with and to test hypotheses about delayed RMDA. Methods: Rod intercept time (RIT) was measured in participants with and without AMD at 5 degrees from the fovea, and macular SD-OCT images were obtained. A deep learning model was trained with anatomically restricted information using a single representative B-scan through the fovea of each eye. Mean-occlusion masking was utilized to isolate the relevant imaging features. Results: The model identified hyporeflective outer retinal bands on macular SD-OCT associated with delayed RMDA. The validation mean standard error (MSE) registered to the foveal B-scan localized the lowest error to 0.5 mm temporal to the fovea center, within an overall low-error region across the rod-free zone and adjoining parafovea. Mean absolute error (MAE) on the test set was 4.71 minutes (8.8% of the dynamic range). Conclusions: We report a novel framework for imaging biomarker discovery using deep learning and demonstrate its ability to identify and localize a previously undescribed biomarker in retinal imaging. The hyporeflective outer retinal bands in central macula on SD-OCT demonstrate a structural basis for dysfunctional rod vision that correlates to published histopathologic findings. Translational Relevance: This agnostic approach to anatomic biomarker discovery strengthens the rationale for RMDA as an outcome measure in early AMD clinical trials, and also expands the utility of deep learning beyond automated diagnosis to fundamental discovery.