Diagnostic accuracy of artificial intelligence for obstructive sleep apnea detection: a systematic review - Haghighat, Sara; Joghatayi, Muhammed; Issa, Julien; Azimian, Sarina; Brinz, Janet; Ashkan, Ali; Chaurasia, Akhilanand; Rahimian, Zahra; Sangalli, Linda (BMC medical informatics and decision making, 2025)
Background Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder. Misdiagnosis might lead to several systemic conditions, including hypertension, vascular damage, and cognitive impairment. The gold-standard diagnostic tool for OSA is polysomnography, which is expensive, time-consuming, and not accessible everywhere. Artificial intelligence (AI) algorithms can facilitate diagnosis by detecting patients’ signs and symptoms. In this systematic review, we evaluated the diagnostic accuracy of AI models in detecting sleep apnea. Methods We searched six major databases, PubMed ® , Cochrane, Web of Science, Scopus, Embase, and IEEE Xplore, using keywords related to AI and OSA. Eligible studies focused on adult populations, used in-laboratory PSG as the reference standard, and applied AI models trained on multiple clinical features. Reviews, pediatric studies, and articles lacking accuracy metrics were excluded. From the included articles, data were extracted regarding patients and datasets, type of AI model applied, accuracy report, and explainability of the AI model. A risk of bias assessment was done using the QUADAS-2 checklist. Results Thirteen studies were included in our final analysis. The AI models consisted of deep learning, machine learning, and hybrid models with various architectures. The reported accuracy of studies ranged from 67.03 to 98.6%, with the highest being related to hybrid and deep learning models. Risk of bias assessment showed that 7 of the studies had a low risk of bias, indicating high reliability. Conclusions AI-driven models, particularly deep learning and hybrid architectures, show significant promise in diagnosing obstructive sleep apnea. However, challenges such as transparency, explainability, and variability in performance necessitate diverse training datasets to improve generalizability for clinical adoption. Registration of systematic reviews The protocol of this systematic review was registered in PROSPERO (CRD42023453789), available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023453789 .