Advanced Analysis of OCT/OCTA Images for Accurately Differentiating Between Glaucoma and Healthy Eyes Using Deep Learning Techniques.

Abstract

To evaluate the discriminative power of optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) images, identifying the best image combination for differentiating glaucoma from healthy eyes using deep learning (DL) with a convolutional neural network (CNN). This cross-sectional study included 157 subjects contributing 1,106 eye scans. We used en-face images of the superficial and choroid layers for OCTA-based vessel density and OCT-based structural thickness of the macula (M) and optic disc (D). Images were preprocessed, resized, and normalized for CNN analysis. The CNN architecture had two components: one extracted features for each image type (OCT-D, OCT-M, OCTA-D, OCTA-M), while the second combined these features to classify eyes as healthy or glaucomatous. Performance was measured by accuracy, sensitivity, specificity, and area under the curve (AUC). For OCT images, the D+M combination outperformed disc (D) or macula (M) alone in three of the four metrics. For OCTA images, D+M also performed better than D or M alone, with D+M outperforming disc (D) in all criteria. Across all metrics for combined OCT+OCTA images, D+M performed better than D or M alone, and the macula (M) outperformed the disc (D). In disc (D) imaging, OCTA outperformed both OCT and OCT+OCTA in accuracy, sensitivity, and specificity, while OCT+OCTA had a higher AUC. OCTA consistently outperformed OCT and OCT+OCTA across all metrics for combined D+M images. The OCTA D+M combination performed best, followed by the OCT+OCTA D+M combination. When both en-face images are available, OCTA is preferred. Always include both disc and macula images for optimal diagnosis.