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.
METHODS: The curriculum development process involved a gap analysis, comprehensive literature review, and expert consensus through a modified RAND appropriateness method (RAM)/Delphi survey.
RESULTS: The curriculum offers two flexible tracks: a one-year program (Track A) and a two-year program (Track B), accommodating varied educational pathways and healthcare system structures across Asia. Key features of the curriculum include detailed learning outcomes, competency-based educational content, and recommendations for teaching and learning activities. The assessment strategy incorporates summative and formative methods, with standard setting and program evaluation guidelines. The curriculum also provides recommendations for program accreditation, fellow-faculty ratios, and funding considerations.
CONCLUSIONS: The Asian adult sleep medicine fellowship training curriculum provides a standardized yet adaptable framework for sleep medicine education across diverse Asian healthcare landscapes. By emphasizing flexibility and customization while maintaining high training standards, the curriculum aims to bridge the gap in sleep medicine training across Asia, ultimately improving the quality of sleep healthcare and patient outcomes throughout the region.