Accuracy of deep learning in diagnosis of apnea syndrome: a systematic review and meta-analysis.

ABSTRACT:

Objectives

This systematic review and meta-analysis was carried out to elucidate the accuracy of image-based deep learning (DL) methods in the real-time detection of obstructive sleep apnea syndrome (OSAS).

Methods

A systematic search was conducted for studies published since database establishment up to September 25, 2025, across databases including PubMed, Embase, Web of Science, and the Cochrane Library. The included studies were assessed for risk of bias by using the QUADAS-2 tool. During this meta-analysis, a bivariate mixed-effects model was employed and only synthesized the results from the meta-analysis of the validation sets. Meanwhile, subgroup analyses were conducted based on the generation methods of the validation sets.

Results

A total of 39 original stud

SUBMITTER: Saiyitijiang A 

PROVIDER: S-EPMC12702770 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature