Nature and science of sleep · 2025
AI-Assisted Awake Endoscopic Video Analysis for Obstructive Sleep Apnea Detection.
СтатьяНазвание и аннотация: Перевод в очереди
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Lai Wen-Sen, Li Ting-Wei, Kuo Chung-Feng Jeffrey, Liu Shao-Cheng
Аннотация
OBJECTIVE: Obstructive sleep apnea (OSA) is a common sleep disorder characterized by upper airway obstruction during sleep, leading to hypoxia and serious health consequences. Conventional diagnostic methods such as polysomnography and drug-induced sleep endoscopy (DISE) are often costly, invasive, or time-consuming. This study aimed to develop a safe, rapid, and fully automated AI-assisted platform for OSA detection using nasopharyngoscopic videos acquired during wakefulness, and to propose diagnostic criteria comparable to the apnea-hypopnea index (AHI).
METHODS: Flexible nasopharyngoscopic videos of supine, awake patients were analyzed using an AI system comprising four Xception-based image classifiers to identify scan boundaries and classify anatomical regions (nasopharynx, velopharyngeal/oropharyngeal \[VO] wall, tongue base, and epiglottis \[TE]). Five U-Net-based semantic segmentation models were then applied to extract quantitative airway features. Key variables-including maximum and minimum airway cross-sectional areas, VO wall area ratio, and TE ratio-were entered into a support vector regression model to predict OSA. A total of 103 clinical samples (59 non-OSA, 44 OSA) were analyzed, with 35 cases reserved for testing.
RESULTS: Classification accuracy for nostril, VO/TE, vocal fold, and nasopharynx regions was 100%, 95.8%, 95%, and 98.5%, respectively. The mean intersection over union (IoU) for segmentation models reached 82.72%. The prediction model achieved 97.14% accuracy on the test set. OSA-associated thresholds were identified-VO wall area ratio < 0.41 and TE ratio > 36.97-all comparable to AHI-based diagnosis. The complete diagnostic workflow, including video upload, classification, segmentation, and prediction, was completed in an average of 85 seconds.
CONCLUSION: This study is the first to implement a fully automated, AI-based dynamic endoscopic video analysis for OSA detection in awake patients. The system accurately predicts OSA and localizes potential obstruction sites in a non-invasive, real-time manner, offering a practical outpatient screening tool to help select candidates who require further evaluation with polysomnography or DISE.
METHODS: Flexible nasopharyngoscopic videos of supine, awake patients were analyzed using an AI system comprising four Xception-based image classifiers to identify scan boundaries and classify anatomical regions (nasopharynx, velopharyngeal/oropharyngeal \[VO] wall, tongue base, and epiglottis \[TE]). Five U-Net-based semantic segmentation models were then applied to extract quantitative airway features. Key variables-including maximum and minimum airway cross-sectional areas, VO wall area ratio, and TE ratio-were entered into a support vector regression model to predict OSA. A total of 103 clinical samples (59 non-OSA, 44 OSA) were analyzed, with 35 cases reserved for testing.
RESULTS: Classification accuracy for nostril, VO/TE, vocal fold, and nasopharynx regions was 100%, 95.8%, 95%, and 98.5%, respectively. The mean intersection over union (IoU) for segmentation models reached 82.72%. The prediction model achieved 97.14% accuracy on the test set. OSA-associated thresholds were identified-VO wall area ratio < 0.41 and TE ratio > 36.97-all comparable to AHI-based diagnosis. The complete diagnostic workflow, including video upload, classification, segmentation, and prediction, was completed in an average of 85 seconds.
CONCLUSION: This study is the first to implement a fully automated, AI-based dynamic endoscopic video analysis for OSA detection in awake patients. The system accurately predicts OSA and localizes potential obstruction sites in a non-invasive, real-time manner, offering a practical outpatient screening tool to help select candidates who require further evaluation with polysomnography or DISE.