超声波预测使用机器学习模型的药物诱导睡眠内镜发现
Samuel E Jones1,2, Natalie Aw3, Molly Acord3
1Department of Otorhinolaryngology, University of Maryland Medical Center, Baltimore, Maryland, U.S.A.
The Laryngoscope
|January 29, 2025
概括
超声波测量可以预测睡眠期间的呼吸道崩模式,为药物诱导睡眠内镜 (DISE) 提供了替代方案. 这项研究表明,超声波使用回归和机器学习模型准确地识别了崩的严重程度.
科学领域:
- 医疗成像医学成像
- 睡眠医学 睡眠医学
- 耳鼻喉科 耳鼻喉科 耳鼻喉科
背景情况:
- 药物诱导睡眠内镜 (DISE) 是评估气道崩的标准,但有局限性.
- 超声波为气道测量提供了一个低风险,客观的方法.
- 使用超声波预测崩模式可以改进目前的诊断方法.
研究的目的:
- 为了确定超声波衍生的解剖学测量来预测气道崩.
- 为了将超声波测量与VOTE (皮膜,口腔大,舌头,表) 的崩标准相关联.
- 为了评估超声波在评估睡眠期间呼吸道崩严重性的准确性.
主要方法:
- 对20名成年患者进行了超声波,这些患者在清醒和镇静状态下进行了超声波.
- 同时内镜在药物诱导睡眠 (DISE) 期间进行.
- 通用最小平方回归 (GLS) 和机器学习 (ML) 模型分析了超声波测量以预测崩 (Pc) 和VOTE分数.
主要成果:
- 超声波测量显示了与内镜气道崩的显著关联 (肯德尔相关性).
- 超声波模型显示了超声波特征与塌严重程度之间的中度至强度相关性 (R2adj 0.530.82).
- ML模型准确地预测了五个分站中的四个分站的二进制VOTE分数 (F1分数>0.65),侧面面膜塌是最准确的 (F1=0.93).
结论:
- 超声波是确定药物诱导睡眠期间呼吸道崩特征的可靠工具.
- 回归 (GLS) 和机器学习 (ML) 模型在使用超声波测量预测崩严重程度方面显示出前景.
- 基于超声波的气道评估可能为DISE提供了有价值的替代方案或辅助方案.
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