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

Abstract

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.

Keywords: age-related macular degeneration; biomarker; deep learning; drusen; rod-mediated dark adaptation; spectral domain optical coherence tomography.

Figure 1.

Concept diagram of framework for biomarker discovery using deep learning. The overall framework is shown in (A). After aligning spectral domain optical coherence tomography (SD-OCT) images, separate datasets are created and different convolutional neural networks (CNN) deep learning models are trained (B). The frozen models with the best performance, lowest validation loss, are systematically perturbed with mean occlusion in the test set and perturbations increasing and decreasing the predictions are shown in green and red, respectively (C).

Figure 2.

Performance of deep learning models by anatomic location. Training curves for two different anatomic locations (blue and orange curves) (A) by root mean standard error (RMSE) and mean absolute error (MAE); shaded region shows 95% confidence intervals by repeated training sessions. The anatomic positions are indicated by the two dotted lines of corresponding color in panel (B). Lowest error on foveal B-scan by millimeters eccentricity and RMSE loss with lower being higher performance B. The fovea is labeled with the white arrow.

Figure 3.

Visualization of deep learning features from the test set. The original spectral domain optical coherence tomography (SD-OCT) scan in mm used by the deep learning model to predict rod intercept time (RIT) are shown in (A, D, G). Panels (B, E, H) show the magnitude of the difference between the perturbed and baseline predictions caused by occlusion of each possible pixel position, with red showing elongation and blue showing shortening of the RIT. The corresponding overlays are shown in (C, F, I) in relation to the ellipsoid zone (EZ).

Figure 4.

Correlation of hyporeflective bands with rod intercept time (RIT). Panel (A) shows a reference image of the external limiting membrane (ELM), the ellipsoid zone (EZ), the interdigitation zone (IZ), and the retinal pigment epithelium-Bruch's membrane (RPE-BrM) on spectral domain optical coherence tomography (SD-OCT). Three examples of low RIT (B), medium RIT (C), and high RIT (D) sampled randomly from the test set are shown with high resolution insets (red boxes) and the RIT in minutes. The IZ, which is apparent in the reference figure (also from this population), is not apparent in any of the randomly sampled figures. Further the gap between the RPE-BrM and the EZ is more hyper-reflective in C, D than in B. Blurring of hyporeflective bands superficial and deep to the EZ correlates with RIT.