Deep Learning for Diagnosing and Segmenting Choroidal Neovascularization in OCT Angiography in a Large Real-World Data Set

PURPOSE: To diagnose and segment choroidal neovascularization (CNV) in a real-world multicenter clinical OCT angiography (OCTA) data set using deep learning. METHODS: A total of 105,66 OCTA scans from 3135 eyes, including 4701 with CNV and 5865 without, were collected in five eye clinics. Both 3 × 3-mm and 6 × 6-mm scans of the central and temporal macula were included. Scans with CNV were collected from multiple diseases, and scans without CNV were collected from both healthy controls and those with multiple diseases. No scans were removed during training or testing due to poor quality. The trained hybrid multitask convolutional neural network outputs a CNV diagnosis and membrane segmentation, respectively. RESULTS: The model demonstrated a highly accurate CNV diagnosis (area under receiver operating characteristic curve = 0.97), achieving a sensitivity of 95% at 95% specificity. The model also correctly segmented CNV lesions (F1 score = 0.78 ± 0.19). Additionally, model performance was comparable on both high-definition 3 × 3-mm scans and low-definition 6 × 6-mm scans. The model did not suffer large performance variations under different diseases. We also show that a subclinical lesion in a patient with neovascular age-related macular degeneration can be monitored over a multiyear time frame using our approach. CONCLUSIONS: The proposed method can accurately diagnose and segment CNV in a large real-world clinical data set. TRANSLATIONAL RELEVANCE: The algorithm could enable automated CNV screening and quantification in the clinic, which will help improve CNV diagnosis and treatment evaluation.