Development of Deep Learning Models to Predict Best-Corrected Visual Acuity from Optical Coherence Tomography
Michael Kawczynski, Thomas Bengtsson, Jian S. Dai, J. Jill Hopkins, Simon S. Gao, Jeffrey R. Willis
Abstract
Purpose: To develop deep learning (DL) models to predict best-corrected visual acuity (BCVA) from optical coherence tomography (OCT) images from patients with neovascular age-related macular degeneration (nAMD). Methods: Retrospective analysis of OCT images and associated BCVA measurements from the phase 3 HARBOR trial (NCT00891735). DL regression models were developed to predict BCVA at the concurrent visit and 12 months from baseline using OCT images. Binary classification models were developed to predict BCVA of Snellen equivalent of <20/40, <20/60, and ≤20/200 at the concurrent visit and 12 months from baseline. Results: The regression model to predict BCVA at the concurrent visit had R2 = 0.67 (root-mean-square error [RMSE] = 8.60) in study eyes and R2 = 0.84 (RMSE = 9.01) in fellow eyes. The best classification model to predict BCVA at the concurrent visit had an area under the receiver operating characteristic curve (AUC) of 0.92 in study eyes and 0.98 in fellow eyes. The regression model to predict BCVA at month 12 using baseline OCT had R2 = 0.33 (RMSE = 14.16) in study eyes and R2 = 0.75 (RMSE = 11.27) in fellow eyes. The best classification model to predict BCVA at month 12 had AUC = 0.84 in study eyes and AUC = 0.96 in fellow eyes. Conclusions: DL shows promise in predicting BCVA from OCTs in nAMD. Further research should elucidate the utility of models in clinical settings. Translational Relevance: DL models predicting BCVA could be used to enhance understanding of structure–function relationships and develop more efficient clinical trials.