Estimating the risk of obstructive sleep apnea during wakefulness using facial images: A review
Bibliographic record
Abstract
Obstructive sleep apnea (OSA) is a chronic sleep-related breathing disorder associated with cardiovascular diseases, cognitive impairments, and an increased risk of accidents. Although polysomnography (PSG) stands as the gold standard for diagnosing OSA, its limitations – such as being cumbersome, expensive, and having long waitlists – have motivated researchers to develop alternative screening methods. Facial photography, serving as an accessible modality, offers insights into anatomical structures linked to OSA. This study aims to comprehensively review existing research on leveraging facial images to estimate OSA severity. We first investigate the physiological intersections between OSA and craniofacial structures. Furthermore, we discuss extracted facial image features, employed feature selection techniques, and details of developed models aimed at detecting OSA severity. Through a comprehensive discussion of current findings and limitations within the field, we aim to shed light on critical gaps necessitating attention in future research directions. • First review investigating the association between facial photography and OSA. • Offering comprehensive list of models and features employed from facial photography. • Exploring gaps and limitations and delivering insights for future directions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".