Deep Learning-Based Prediction of Individual Geographic Atrophy Progression from a Single Baseline OCT.

ABSTRACT:

Objective

To identify the individual progression of geographic atrophy (GA) lesions from baseline OCT images of patients in routine clinical care.

Design

Clinical evaluation of a deep learning-based algorithm.

Subjects

One hundred eighty-four eyes of 100 consecutively enrolled patients.

Methods

OCT and fundus autofluorescence (FAF) images (both Spectralis, Heidelberg Engineering) of patients with GA secondary to age-related macular degeneration in routine clinical care were used for model validation. Fundus autofluorescence images were annotated manually by delineating the GA area by certified readers of the Vienna Reading Center. The annotated FAF images were anatomically registered in an automated manner to the corresponding OCT scans, resulting in 2-dimensio

SUBMITTER: Mai J 

PROVIDER: S-EPMC11000109 | biostudies-literature | 2024 Jul-Aug

REPOSITORIES: biostudies-literature