Applications of machine learning in glaucoma diagnosis based on tabular data: a systematic review.

ABSTRACT: Glaucoma is a leading cause of irreversible blindness, necessitating early and accurate diagnosis to prevent vision loss. Traditional diagnostic methods often suffer from subjectivity and variability, emphasizing the need for more reliable approaches. This study evaluates the application of machine learning (ML) techniques in glaucoma diagnosis, analyzing their effectiveness and identifying the most promising methods and datasets. A systematic review of five major databases was conducted, selecting 35 studies based on predefined criteria. The findings reveal that structured data, including optical coherence tomography (OCT), visual field (VF) tests, and demographic factors, significantly enhance diagnostic accuracy. ML models such as support vector machine (SVM), deep learning (DL), random

SUBMITTER: Shahriari MH 

PROVIDER: S-EPMC12315471 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature