In vivo identification of angle dysgenesis and its relation to genetic markers associated with glaucoma using artificial intelligence.

ABSTRACT:

Purpose

To predict the presence of angle dysgenesis on anterior-segment optical coherence tomography (ADoA) by using deep learning (DL) and to correlate ADoA with mutations in known glaucoma genes.

Participants

In total, 800 high-definition anterior-segment optical coherence tomography (AS-OCT) images were included, of which 340 images were used to build the machine learning (ML) model. Images used to build the ML model included 170 scans of primary congenital glaucoma (16 patients), juvenile-onset open-angle glaucoma (62 patients), and adult-onset primary open-angle glaucoma eyes (37 patients); the rest were controls (n = 85). The genetic validation dataset consisted of another 393 images of patients with known mutations that were compared with 320 images of healthy control

SUBMITTER: Gupta V 

PROVIDER: S-EPMC11001234 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature