Artificial intelligence facial recognition of obstructive sleep apnea: a Bayesian meta-analysis.

Autor: Gao EY; Department of Otorhinolaryngology-Head & Neck Surgery, Singapore General Hospital (SGH), Singapore, Singapore.; Department of Otorhinolaryngology, Sengkang General Hospital, Singapore, Singapore.; Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.; SingHealth Duke-NUS Sleep Centre, Singapore, Singapore., Tan BKJ; Department of Otorhinolaryngology-Head & Neck Surgery, Singapore General Hospital (SGH), Singapore, Singapore. benjamintankyejyn@u.nus.edu.; Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. benjamintankyejyn@u.nus.edu.; SingHealth Duke-NUS Sleep Centre, Singapore, Singapore. benjamintankyejyn@u.nus.edu., Tan NKW; Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore., Ng ACW; Department of Otorhinolaryngology-Head & Neck Surgery, Singapore General Hospital (SGH), Singapore, Singapore.; SingHealth Duke-NUS Sleep Centre, Singapore, Singapore.; Surgery Academic Clinical Program, SingHealth, Singapore, Singapore., Leong ZH; Department of Otorhinolaryngology-Head & Neck Surgery, Singapore General Hospital (SGH), Singapore, Singapore.; Surgery Academic Clinical Program, SingHealth, Singapore, Singapore., Phua CQ; Department of Otorhinolaryngology, Sengkang General Hospital, Singapore, Singapore.; SingHealth Duke-NUS Sleep Centre, Singapore, Singapore.; Surgery Academic Clinical Program, SingHealth, Singapore, Singapore., Loh SRH; Department of Otorhinolaryngology-Head & Neck Surgery, Singapore General Hospital (SGH), Singapore, Singapore.; SingHealth Duke-NUS Sleep Centre, Singapore, Singapore.; Surgery Academic Clinical Program, SingHealth, Singapore, Singapore., Uataya M; Siriraj Piyamaharajkarun Hospital, Bangkok, Thailand., Goh LC; Department of Otorhinolaryngology, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia., Ong TH; SingHealth Duke-NUS Sleep Centre, Singapore, Singapore.; Department of Respiratory and Critical Care Medicine, Singapore General Hospital, Singapore, Singapore., Leow LC; SingHealth Duke-NUS Sleep Centre, Singapore, Singapore.; Department of Respiratory and Critical Care Medicine, Singapore General Hospital, Singapore, Singapore., Huang GB; School of Automation, Southeast University, Nanjing, China.; Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Nanjing, China.; Mind PointEye, Singapore, Singapore., Toh ST; Department of Otorhinolaryngology-Head & Neck Surgery, Singapore General Hospital (SGH), Singapore, Singapore. toh.song.tar@singhealth.com.sg.; Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. toh.song.tar@singhealth.com.sg.; SingHealth Duke-NUS Sleep Centre, Singapore, Singapore. toh.song.tar@singhealth.com.sg.; Surgery Academic Clinical Program, SingHealth, Singapore, Singapore. toh.song.tar@singhealth.com.sg.
Jazyk: angličtina
Zdroj: Sleep & breathing = Schlaf & Atmung [Sleep Breath] 2024 Nov 30; Vol. 29 (1), pp. 36. Date of Electronic Publication: 2024 Nov 30.
DOI: 10.1007/s11325-024-03173-3
Abstrakt: Purpose: Conventional 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.
Methods: Two 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.
Results: From 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.
Conclusion: AI 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.
Competing Interests: Declarations. Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors. Informed consent: This type of study does not require informed consent. Conflict of interest: The authors have no conflicts of interest to declare.
(© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)
Databáze: MEDLINE
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