Artificial intelligence for OCTA-based disease activity prediction in age-related macular degeneration.

Abstract

Purpose: We hypothesize that OCTA-visualized vascular morphology may be a predictor of CNV status in AMD. We thus evaluated the use of AI to predict different stages of AMD disease based on OCTA en-face 2D projections scans. Methods: Retrospective cross-sectional study based on collected 2D OCTA data from 310 high-resolution scans. Based on OCT B-scan fluid and clinical status, OCTA was classified as normal, dry AMD, wet AMD active and wet-AMD in remission with no signs of activity. Two human experts graded the same test set and a consensus grading between 2 experts was used for the prediction of 4 categories. Results: The AI can achieve 80.36% accuracy on a four-category grading task with 2D OCTA projections. The sensitivity of prediction by AI was: 0.7857 (active), 0.7142 (remission), 0.9286 (dry AMD), and 0.9286 (normal) and the specificity was 0.9524, 0.9524, 0.9286, and 0.9524, respectively. The sensitivity of prediction by human experts was: 0.4286 active CNV, 0.2143 remission, 0.8571 dry AMD, and 0.8571 normal with specificity of 0.7619, 0.9286, 0.7857, 0.9762 respectively. The overall AI classification prediction was significantly better than the human (odds ratio=1.95, p=0.0021). Conclusion: Our data shows that CNV morphology can be used to predict disease activity by AI; Longitudinal studies are needed to better understand the evolution of CNV and features that predict reactivation. Future studies will be able to evaluate the additional predicative value of OCTA on top of other imaging characteristics (i.e., fluid location on OCT B scans) to help predict response to treatment.