Deep Learning Approaches to Predict Geographic Atrophy Progression Using Three-Dimensional OCT Imaging.

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To evaluate the performance of various approaches of processing three-dimensional (3D) optical coherence tomography (OCT) images for deep learning models in predicting area and future growth rate of geographic atrophy (GA) lesions caused by age-related macular degeneration (AMD). The study used OCT volumes of GA patients/eyes from the lampalizumab clinical trials (NCT02247479, NCT02247531, NCT02479386); 1219 and 442 study eyes for model development and holdout performance evaluation, respectively. Four approaches were evaluated: (1) en-face intensity maps; (2) SLIVER-net; (3) a 3D convolutional neural network (CNN); and (4) en-face layer thickness and between-layer intensity maps from a segmentation model. The processed OCT images and maps served as input for CNN models to predict baseline GA lesion area size and annualized growth rate. For the holdout dataset, the Pearson correlation coefficient squared (r2) in the GA growth rate prediction was comparable for all the evaluated approaches (0.33∼0.35). In baseline lesion size prediction, prediction performance was comparable (0.9∼0.91) except for the SLIVER-net (0.83). Prediction performance with only the thickness map of the ellipsoid zone (EZ) or retinal pigment epithelium (RPE) layer individually was inferior to using both. Addition of other layer thickness or intensity maps did not improve the prediction performance. All explored approaches had comparable performance, which might have reached a plateau to predict GA growth rate. EZ and RPE layers appear to contain the majority of information related to the prediction. Our study provides important insights on the utility of 3D OCT images for GA disease progression predictions.