改善OSA诊断:一种基于人工智能的方法来分析DISE视频,以评估阻塞部位和严重程度
Ting-So Chang1, Chen-You Liu2, Ying-Hui Lai3
1Department of Otorhinolaryngology, Head and Neck Surgery, Taipei Medical University Hospital, Taiwan; Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taiwan.
Journal of the Formosan Medical Association = Taiwan yi zhi
|January 17, 2026
概括
一个人工智能系统在药物诱导睡眠内镜 (DISE) 期间自动化上呼吸道阻塞分析. 这提供了一个更快,更客观的方式来评估阻塞性睡眠呼吸暂停 (OSA) 的严重程度和指导治疗.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 睡眠医学 睡眠医学
背景情况:
- 阻塞性睡眠呼吸暂停 (OSA) 是一种常见的睡眠障碍.
- 药物诱导睡眠内镜 (DISE) 可视化呼吸道崩,但依赖于主观的VOTE分类.
- 目前的方法耗时,缺乏客观性.
研究的目的:
- 开发一个基于人工智能的自动化系统,用于分析DISE期间的上呼吸道阻塞.
- 从连续的DISE图像中确定皮膜和上衣阻塞比率.
- 为了提供更有效和客观的评估气道崩.
主要方法:
- 开发了一个人工智能系统,用于解剖区域分类和阻塞率估计.
- 165个DISE视频用于培训 (157) 和验证 (8).
- 深度感知补偿和基于距离的框架选择提高了复杂区域的准确性.
主要成果:
- 在解剖学分类中获得了高精度 (Velum: 82.47%,Epiglottis: 85.48%).
- 在舌头底部 (51.78%至78.32%) 和口腔大 (48.38%至77.66%) 的障碍分级精度显著提高.
- 发现了81.07%的准确度,提供了详细的评估.
结论:
- 人工智能系统在DISE期间提供了对上呼吸道阻塞的准确和客观分析.
- 这种自动化方法可以改善对阻塞性睡眠呼吸暂停的评估.
- 该系统为主观的VOTE分类提供了一个更有效的替代方案.
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