Deep-learning based automated quantification of critical optical coherence tomography features in neovascular age-related macular degeneration.

ABSTRACT:

Purpose

To validate a deep learning algorithm for automated intraretinal fluid (IRF), subretinal fluid (SRF) and neovascular pigment epithelium detachment (nPED) segmentations in neovascular age-related macular degeneration (nAMD).

Methods

In this IRB-approved study, optical coherence tomography (OCT) data from 50 patients (50 eyes) with exudative nAMD were retrospectively analysed. Two models, A1 and A2, were created based on gradings from two masked readers, R1 and R2. Area under the curve (AUC) values gauged detection performance, and quantification between readers and models was evaluated using Dice and correlation (R²) coefficients.

Results

The deep learning-based algorithms had high accuracies for all fluid types between all models and readers: per B-

SUBMITTER: Borrelli E 

PROVIDER: S-EPMC10858028 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature