Three-Dimensional Quantification of Macular OCT Alterations Improves the Diagnostic Performance of Artificial Intelligence Models.

ABSTRACT:

Purpose

To evaluate the performance of state-of-the-art semantic segmentation methods on OCT data for age-related macular degeneration (AMD). We measured variability between annotators to quantify differences in ground truth arising from personal bias.

Methods

From 94 patients suffering from exudative neovascular AMD (nAMD), 24 volume scans (49 slices each) were selected. Trained members of a reading center for AMD created pixel-wise masks for 12 retinal layers and two pathological labels (fluid, hyperreflective material) to benchmark two-dimensional (2D) and three-dimensional (3D) segmentation models on clinical data. Models were evaluated using fivefold cross-validation, and the best model was used to quantify errors between ground truth and predictions.

Results

The

SUBMITTER: Heine L 

PROVIDER: S-EPMC12279069 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature