1. Development of Deep Learning Models to Predict Best-Corrected Visual Acuity from Optical Coherence Tomography

    Development of Deep Learning Models to Predict Best-Corrected Visual Acuity from Optical Coherence Tomography

    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 ...

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