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

Purpose: 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).

Methods: 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).