Evaluation of Cone- and Rod-Mediated Parameters in Dark Adaptation Testing as Outcome Measures in Age-Related Macular Degeneration.

PURPOSE: To investigate the suitability of 6 rod- or cone-mediated dark adaptation (DA) parameters as outcome measures for clinical trials in age-related macular degeneration (AMD), including their retest reliability, association with age and disease severity, and measurable longitudinal change over time. DESIGN: Prospective, longitudinal study (Clinicaltrials.gov: NCT01352975). PARTICIPANTS: A total of 191 patients with AMD and older participants followed longitudinally over 5 years. METHODS: Dark adaptation testing was performed using the AdaptDx dark adaptometer with a maximum test time of 40 minutes. A 2-part exponential-linear curve was fitted to obtain values for cone decay, cone plateau, time to rod-cone break, rod intercept time (RIT), rod adaptation rate (S2), and area under the curve. Intersession retest reliability was assessed in tests performed within 2 weeks using the Bland-Altman analysis. The relationship of DA parameters with age, AMD severity, and reticular pseudodrusen (RPD) presence was evaluated using linear mixed models. MAIN OUTCOME MEASURES: Retest reliability, association with disease severity, and longitudinal change of 6 DA parameters. RESULTS: A total of 1329 DA curves were analyzed. Rod intercept time was the parameter that showed the greatest reliability (intraclass correlation coefficient of 0.88) and greatest association with age, AMD severity, and RPD (marginal R2 of 0.38), followed by the rod-mediated parameters area under the curve and rod-cone break. Cone plateau appeared constant at lower RIT values but increased with progressive rod dysfunction (RIT > 22.8 minutes) with a slope of 0.07 log units per 10 minutes RIT prolongation. Therefore, it might provide additional information in the advanced stages of AMD. CONCLUSIONS: Age-related macular degeneration severity and RPD presence are each associated with large differences in multiple DA curve parameters. In addition, substantial differences in some parameters occur with age, even accounting for AMD severity and RPD status. This supports the 2-hit hypothesis of age and disease status on DA (and perhaps AMD pathophysiology itself). Of the DA parameters, RIT has the highest retest reliability, closest correlation with AMD severity and RPD, and largest longitudinal changes. This underscores the suitability of RIT as an outcome measure in clinical trials. The cone plateau increases only in advanced stages of kinetic rod dysfunction, indicating rod dysfunction preceding cone dysfunction and degeneration in the temporal sequence of pathology in AMD.

Retinal layer segmentation in optical coherence tomography (OCT) using a 3D deep-convolutional regression network for patients with age-related macular degeneration.

Introduction - Retinal layer segmentation in optical coherence tomography (OCT) images is an important approach for detecting and prognosing disease. Automating segmentation using robust machine learning techniques lead to computationally efficient solutions and significantly reduces the cost of labor-intensive labeling, which is traditionally performed by trained graders at a reading center, sometimes aided by semi-automated algorithms. Although several algorithms have been proposed since the revival of deep learning, eyes with severe pathological conditions continue to challenge fully automated segmentation approaches. There remains an opportunity to leverage the underlying spatial correlations between the retinal surfaces in the segmentation approach. Methods - Some of these proposed traditional methods can be expanded to utilize the three-dimensional spatial context governing the retinal image volumes by replacing the use of 2D filters with 3D filters. Towards this purpose, we propose a spatial-context, continuity and anatomical relationship preserving semantic segmentation algorithm, which utilizes the 3D spatial context from the image volumes with the use of 3D filters. We propose a 3D deep neural network capable of learning the surface positions of the layers in the retinal volumes. Results - We utilize a dataset of OCT images from patients with Age-related Macular Degeneration (AMD) to assess performance of our model and provide both qualitative (including segmentation maps and thickness maps) and quantitative (including error metric comparisons and volumetric comparisons) results, which demonstrate that our proposed method performs favorably even for eyes with pathological changes caused by severe retinal diseases. The Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for patients with a wide range of AMD severity scores (0-11) were within 0.84±0.41 and 1.33±0.73 pixels, respectively, which are significantly better than some of the other state-of-the-art algorithms. Conclusion - The results demonstrate the utility of extracting features from the entire OCT volume by treating the volume as a correlated entity and show the benefit of utilizing 3D autoencoder based regression networks for smoothing the approximated retinal layers by inducing shape based regularization constraints.

Deep Learning-Based Modeling of the Dark Adaptation Curve for Robust Parameter Estimation.

Purpose: This study investigates deep-learning (DL) sequence modeling techniques to reliably fit dark adaptation (DA) curves and estimate their key parameters in patients with age-related macular degeneration (AMD) to improve robustness and curve predictions. Methods: A long-short-term memory autoencoder was used as the DL method to model the DA curve. The performance was compared against the classical nonlinear regression method using goodness-of-fit and repeatability metrics. Experiments were performed to predict the latter portion of the curve using data from early measurements. The prediction accuracy was quantified as the rod intercept time (RIT) prediction error between predicted and actual curves. Results: The two models had comparable goodness-of-fit measures, with root mean squared error (RMSE; SD) = 0.11 (0.04) log-units (LU) for the classical model and RMSE = 0.13 (0.06) LU for the DL model. Repeatability of the curve fits evaluated after introduction of random perturbations, and after performing repeated testing, demonstrated superiority of the DL method, especially among parameters related to cone decay. The DL method exhibited superior ability to predict the curve and RIT using points prior to -2 LU, with 3.1 ± 3.1 minutes RIT prediction error, compared to 19.1 ± 18.6 minutes RIT error for the classical method. Conclusions: The parameters obtained from the DL method demonstrated superior robustness as well as predictability of the curve. These could provide important advances in using multiple DA curve parameters to characterize AMD severity. Translational Relevance: Dark adaptation is an important functional measure in studies of AMD and curve modeling using DL methods can lead to improved clinical trial end points.

Deep Learning-Based Automatic Detection of Ellipsoid Zone Loss in Spectral-Domain OCT for Hydroxychloroquine Retinal Toxicity Screening.

Purpose: Retinal toxicity resulting from hydroxychloroquine use manifests photoreceptor loss and disruption of the ellipsoid zone (EZ) reflectivity band detectable on spectral-domain (SD) OCT imaging. This study investigated whether an automatic deep learning-based algorithm can detect and quantitate EZ loss on SD OCT images with an accuracy comparable with that of human annotations. Design: Retrospective analysis of data acquired in a prospective, single-center, case-control study. Participants: Eighty-five patients (168 eyes) who were long-term hydroxychloroquine users (average exposure time, 14 ± 7.2 years). Methods: A mask region-based convolutional neural network (M-RCNN) was implemented and trained on individual OCT B-scans. Scan-by-scan detections were aggregated to produce an en face map of EZ loss per 3-dimensional SD OCT volume image. To improve the accuracy and robustness of the EZ loss map, a dual network architecture was proposed that learns to detect EZ loss in parallel using horizontal (horizontal mask region-based convolutional neural network [M-RCNNH]) and vertical (vertical mask region-based convolutional neural network [M-RCNNV]) B-scans independently. To quantify accuracy, 10-fold cross-validation was performed. Main Outcome Measures: Precision, recall, intersection over union (IOU), F1-score metrics, and measured total EZ loss area were compared against human grader annotations and with the determination of toxicity based on the recommended screening guidelines. Results: The combined projection network demonstrated the best overall performance: precision, 0.90 ± 0.09; recall, 0.88 ± 0.08; and F1 score, 0.89 ± 0.07. The combined model performed superiorly to the M-RCNNH only model (precision, 0.79 ± 0.17; recall, 0.96 ± 0.04; IOU, 0.78 ± 0.15; and F1 score, 0.86 ± 0.12) and M-RCNNV only model (precision, 0.71 ± 0.21; recall, 0.94 ± 0.06; IOU, 0.69 ± 0.21; and F1 score, 0.79 ± 0.16). The accuracy was comparable with the variability of human experts: precision, 0.85 ± 0.09; recall, 0.98 ± 0.01; IOU, 0.82 ± 0.12; and F1 score, 0.91 ± 0.06. Automatically generated en face EZ loss maps provide quantitative SD OCT metrics for accurate toxicity determination combined with other functional testing. Conclusions: The algorithm can provide a fast, objective, automatic method for measuring areas with EZ loss and can serve as a quantitative assistance tool to screen patients for the presence and extent of toxicity.