An AI model to estimate visual acuity based solely on cross-sectional OCT imaging of various diseases.

ABSTRACT:

Purpose

To develop an artificial intelligence (AI) model for estimating best-corrected visual acuity (BCVA) using horizontal and vertical optical coherence tomography (OCT) scans of various retinal diseases and examine factors associated with its accuracy.

Methods

OCT images and associated BCVA measurements from 2,700 OCT images (accrued from 2004 to 2018 with an Atlantis, Triton; Topcon, Tokyo, Japan) of 756 eyes of 469 patients and their BCVA were retrospectively analysed. For each eye, one horizontal and one vertical OCT scan in cross-line mode were used. The GoogLeNet architecture was implemented. The coefficient of determination (R²), root mean square error (RMSE) and mean absolute error (MAE) were computed to evaluate the performance of the trained network.<

SUBMITTER: Inoda S 

PROVIDER: S-EPMC10543844 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature