Diagnostic Accuracy of AI Models in Detecting Different Inherited Retinal Diseases: A Systematic Review and Meta-Analysis.

ABSTRACT:

Purpose

The purpose of this study was to evaluate the diagnostic accuracy of artificial intelligence (AI) models in detecting different types of inherited retinal diseases (IRDs), including retinitis pigmentosa (RP), Stargardt disease, and familial exudative vitreoretinopathy (FEVR).

Methods

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic search of databases, such as ScienceDirect, PubMed, Scopus, WoS, and IEEE, was conducted. Studies were included in meta-analysis if data on true positive, false positive, true negative, and false negative could be extracted for AI models, diagnosing IRDs. Data extraction included study characteristics, diagnostic accuracy metrics, and imaging modalities, with QUADAS-2 used for r

SUBMITTER: Ashrafi S 

PROVIDER: S-EPMC12312766 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature