Machine learning OCT predictors of progression from intermediate age-related macular degeneration to geographic atrophy and vision loss.

ABSTRACT:

Objective

To describe optical coherence tomography (SD-OCT) features, age, gender, and systemic variables that may be used in machine/deep learning studies to identify high-risk patient subpopulations with high risk of progression to geographic atrophy (GA) and visual acuity (VA) loss in the short term.

Design

prospective, longitudinal study.

Subjects

We analyzed imaging data from patients with iAMD (N= 316) enrolled in Age-Related Eye Disease Study 2 (AREDS2) Ancillary SD-OCT with adequate SD-OCT imaging for repeated measures.

Methods

Qualitative and quantitative multimodal variables from the database were derived at each yearly visit over 5 years. Based on statistical analyses developed in the field of cardiology, an algorithm was developed and used to select

SUBMITTER: Lad E 

PROVIDER: S-EPMC9161427 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature