Artificial intelligence in glaucoma: posterior segment optical coherence tomography.

ABSTRACT:

Purpose of review

To summarize the recent literature on deep learning (DL) model applications in glaucoma detection and surveillance using posterior segment optical coherence tomography (OCT) imaging.

Recent findings

DL models use OCT derived parameters including retinal nerve fiber layer (RNFL) scans, macular scans, and optic nerve head (ONH) scans, as well as a combination of these parameters, to achieve high diagnostic accuracy in detecting glaucomatous optic neuropathy (GON). Although RNFL segmentation is the most widely used OCT parameter for glaucoma detection by ophthalmologists, newer DL models most commonly use a combination of parameters, which provide a more comprehensive approach. Compared to DL models for diagnosing glaucoma, DL models predicting glaucoma progre

SUBMITTER: Gutierrez A 

PROVIDER: S-EPMC10090343 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature