ArticleSleep & breathing = Schlaf & Atmung2022
Detecting obstructive sleep apnea by craniofacial image-based deep learning.
Article in Sleep & breathing = Schlaf & Atmung, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 3 of them syntheses that pooled it.
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The trial behind it
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Who cites it
10 citing papers in PubMed, 3 syntheses or guidelines pooled it, 14 citations in OpenAlex.
- Performance of AI-Based Screening Tools for Obstructive Sleep Apnea Across Apnea-Hypopnea Index Thresholds: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Diagnostic accuracy of artificial intelligence for obstructive sleep apnea detection: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Artificial intelligence facial recognition of obstructive sleep apnea: a Bayesian meta-analysis.Sleep & breathing = Schlaf & Atmung · 2024Pooled it
- Three-class obstructive sleep apnea severity assessment: a parallel AHI and ODI explainable artificial intelligence framework using craniofacial-enriched clinical data.Sleep & breathing = Schlaf & Atmung · 2026Article
- An Exploration of Craniofacial Features in Obstructive Sleep Apnea Using the Explainable Deep Learning Approaches.Nature and science of sleep · 2026Article
- The Impact of Diagnostic Imaging on Obstructive Sleep Apnea: Feedback from a Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Classification of sleep apnea syndrome using the spectrograms of EEG signals and YOLOv8 deep learning model.PeerJ. Computer science · 2025Article
- Predicting obstructive sleep apnea severity from craniofacial images using ensemble machine learning models.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Prediction of obstructive sleep apnea using deep learning in 3D craniofacial reconstruction.Journal of thoracic disease · 2023Article
- Causal associations between obstructive sleep apnea and COVID-19: A bidirectional Mendelian randomization study.Sleep medicine · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
objectivesThis study aimed to develop a deep learning-based model to detect obstructive sleep apnea (OSA) using craniofacial photographs.
methodsParticipants referred for polysomnography (PSG) were recruited consecutively and randomly divided into the training, validation, and test groups for model development and evaluation. Craniofacial photographs were taken from five different angles (front, right 90° profile, left 90° profile, right 45° profile, and left 45° profile) and inputted to the convolutional neural networks. The neural networks extracted features from photographs and outputted the probabilities of the presence of the disease. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated using PSG diagnosis as the reference standard. These analyses were repeated using two apnea-hypopnea index thresholds (≥ 5 and ≥ 15events/h).
resultsA total of 393 participants were enrolled. Using the operating point with maximum sum of sensitivity and specificity, the model of the photographs exhibited an AUC of 0.916 (95% confidence interval [CI], 0.847-0.960) with a sensitivity of 0.95 and a specificity of 0.80 at an AHI threshold of 5 events/h; an AUC of 0.812 (95% CI, 0.729-0.878) with a sensitivity of 0.91 and a specificity of 0.73 at an AHI threshold of 15 events/h.
conclusionsThe results suggest that combining craniofacial photographs and deep learning techniques can help detect OSA automatically. The model may have potential utility as a tool to assess OSA probability in clinics or screen for OSA in the community.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.