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

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 an

SUBMITTER: Kawczynski MG 

PROVIDER: S-EPMC7488630 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature