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

Abstract

Purpose: 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).

Methods: 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.

Results: 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.

Conclusions: 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.

Translational relevance: Our study provides important insights on the utility of 3D OCT images for GA disease progression predictions.

Figure 1.

Overview of the preprocessing steps of OCT images and CNN model architectures for the four approaches evaluated in the study.

Figure 2.

Prediction performance of the four approaches on (a) the development dataset (cross-validation) and (b) holdout dataset. Cross-validation performance is given as the mean ± SD (black circles and error bars), as well as individual cross-validation fold performance (colored circles), for the square of Pearson correlation coefficient (r²). Holdout performance is given as r² (± 95% CI, black circles and error bars). Thickness maps for the segmentation outputs were derived as distance between iEZ-iRPE (EZ) and iRPE-oRPE (RPE), where “i” refers to the inner side of and “o” refers to the outer side of the corresponding layers.

Figure 3.

Prediction performance with various combinations of OCT segmentation layer maps. The table on the left indicates which enface feature maps were included as input to 2D CNN. Cross-validation performance is given as the mean ± SD (black circles and error bars), as well as individual cross-validation fold performance (colored circles), for the square of Pearson correlation coefficient (r²). Thickness and intensity maps of the segmentation outputs were derived as distance or mean intensity between layers, where “i” refers to the inner side of and “o” refers to the outer side of the corresponding layers.