Deep-GA-Net for Accurate and Explainable Detection of Geographic Atrophy on OCT Scans

Abstract

Objective: To propose Deep-GA-Net, a 3-dimensional (3D) deep learning network with 3D attention layer, for the detection of geographic atrophy (GA) on spectral domain OCT (SD-OCT) scans, explain its decision making, and compare it with existing methods. Design: Deep learning model development. Participants: Three hundred eleven participants from the Age-Related Eye Disease Study 2 Ancillary SD-OCT Study. Methods: A dataset of 1284 SD-OCT scans from 311 participants was used to develop Deep-GA-Net. Cross-validation was used to evaluate Deep-GA-Net, where each testing set contained no participant from the corresponding training set. En face heatmaps and important regions at the B-scan level were used to visualize the outputs of Deep-GA-Net, and 3 ophthalmologists graded the presence or absence of GA in them to assess the explainability (i.e., understandability and interpretability) of its detections. Main Outcome Measures: Accuracy, area under receiver operating characteristic curve (AUC), area under precision-recall curve (APR). Results: Compared with other networks, Deep-GA-Net achieved the best metrics, with accuracy of 0.93, AUC of 0.94, and APR of 0.91, and received the best gradings of 0.98 and 0.68 on the en face heatmap and B-scan grading tasks, respectively. Conclusions: Deep-GA-Net was able to detect GA accurately from SD-OCT scans. The visualizations of Deep-GA-Net were more explainable, as suggested by 3 ophthalmologists. The code and pretrained models are publicly available at https://github.com/ncbi/Deep-GA-Net. Financial Disclosure(s): The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Keywords

• Deep Learning
• Detection
• Explainability
• GA
• Geographic atrophy
• OCT