Artificial Intelligence to Detect Obstructive Sleep Apnea from Craniofacial Images: A Narrative Review
Bibliographic record
Abstract
Obstructive sleep apnea (OSA) is a chronic disorder associated with serious health consequences, yet many cases remain undiagnosed due to limited access to standard diagnostic tools such as polysomnography. Recent advances in artificial intelligence (AI) have enabled the development of deep convolutional neural networks that analyze craniofacial radiographs, particularly lateral cephalograms, to detect anatomical risk factors for OSA. The goal of this approach is not to replace polysomnography but to identify individuals with a high suspicion of OSA at the primary care or dental level and to guide them toward timely and appropriate diagnostic evaluation. Current studies have demonstrated that AI can recognize patterns of oropharyngeal crowding and anatomical imbalance of the upper airway with high accuracy, often exceeding manual assessment. Furthermore, interpretability analyses suggest that AI focuses on clinically meaningful regions, including the tongue, mandible, and upper airway. Unexpected findings such as predictive signals from outside the airway also suggest AI may detect subtle features associated with age or obesity. Ultimately, integrating AI with cephalometric imaging may support early screening and referral for polysomnography, improving care pathways and reducing delays in OSA treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".