Gao, Esther Yanxin and Tan, Benjamin Kye Jyn and Tan, Nicole Kye Wen and Ng, Adele Chin Wei and Leong, Zhou Hao and Phua, Chu Qin and Loh, Shaun Ray Han and Uataya, Maythad and Goh, Liang Chye and Ong, Thun How and Leow, Leong Chai and Huang, Guang-Bin and Toh, Song Tar (2025) Artificial intelligence facial recognition of obstructive sleep apnea: a Bayesian meta-analysis. Sleep and Breathing, 29 (1). p. 36. ISSN 1520-9512, DOI https://doi.org/10.1007/s11325-024-03173-3.
Full text not available from this repository.Abstract
PurposeConventional obstructive sleep apnea (OSA) diagnosis via polysomnography can be costly and inaccessible. Recent advances in artificial intelligence (AI) have enabled the use of craniofacial photographs to diagnose OSA. This meta-analysis aims to clarify the diagnostic accuracy of this innovative approach.MethodsTwo blinded reviewers searched PubMed, Embase, Scopus, Web of Science, and IEEE Xplore databases, then selected and graded the risk of bias of observational studies of adults (>= 18 years) comparing the diagnostic performance of AI algorithms using craniofacial photographs, versus conventional OSA diagnostic criteria (i.e. apnea-hypopnea index AHI]). Studies were excluded if they detected apneic events without diagnosing OSA. AI models evaluated with a random split test set or k-fold cross-validation were included in a Bayesian bivariate meta-analysis.ResultsFrom 5,147 records, 6 studies were included, containing 10 AI models trained/tested on 1,417/983 participants. The risk of bias was low. AI trained on craniofacial photographs achieved a pooled 84.9% sensitivity (95% credible interval 95% CrI]: 77.1-90.7%) and 71.2% specificity (95% CrI: 60.7-81.4%). Bayesian meta-regression identified deep learning (convolutional neural networks) as the most accurate AI algorithm (91.1% sensitivity, 79.2% specificity) comparable to home sleep apnea tests. AHI cutoffs, OSA prevalence, feature engineering, input data, camera type and informativeness of Bayesian prior did not alter diagnostic accuracy. There was no substantial publication bias.ConclusionAI trained on craniofacial photographs have high diagnostic accuracy and should be considered as a low-cost OSA screening tool. Future work focused on deep learning using smartphone images could improve the feasibility of this approach in primary care.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Sleep disordered breathing; Snoring; Machine learning; Deep learning; Neural networks; Diagnostic test accuracy |
| Subjects: | R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry R Medicine > RF Otorhinolaryngology |
| Divisions: | Faculty of Medicine > Otorhinolaryngology Department |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 24 Mar 2025 06:50 |
| Last Modified: | 24 Mar 2025 06:50 |
| URI: | http://eprints.um.edu.my/id/eprint/47207 |
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