Topographic Clinical Insights From Deep Learning-Based Geographic Atrophy Progression Prediction.

ABSTRACT:

Purpose

To explore the contributions of fundus autofluorescence (FAF) topographic imaging features to the performance of convolutional neural network-based deep learning (DL) algorithms in predicting geographic atrophy (GA) growth rate.

Methods

Retrospective study with data from study eyes from three clinical trials (NCT02247479, NCT02247531, NCT02479386) in GA. The algorithm was initially trained with full FAF images, and its performance was considered benchmark. Ablation experiments investigated the contribution of imaging features to the performance of the algorithms. Three FAF image regions were defined relative to GA: Lesion, Rim, and Background. For No Lesion, No Rim, and No Background datasets, a single region of interest was removed at a time. For Lesion, Rim, and Ba

SUBMITTER: Cluceru J 

PROVIDER: S-EPMC11309039 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature