用不同的机器学习方法对人类测量和超声波测量进行评估,以预测困难的输管:一项前性观察性研究
Gizem Demir Senoglu1, Ezgi Kara Timucin2, Serdar Kırısoglu2
1Department of Anesthesiology and Reanimation, Duzce University Faculty of Medicine, Düzce, Türkiye. gizem123demir@hotmail.com.
BMC anesthesiology
|October 14, 2025
概括
结合超声波测量的机器学习模型显著改善了难以输入管道的预测. 这提供了一个更客观的术前呼吸道评估工具,以提高麻醉期间的患者安全.
科学领域:
- 麻醉学 麻醉学
- 医疗信息学 医疗信息学
- 超声波成像 超声波成像
背景情况:
- 难以输管存在重大风险,增加患者的发病率和死亡率.
- 难以输管的预测工具对于提高患者安全至关重要.
- 机器学习 (ML) 为医疗保健中先进的预测建模提供了潜力.
研究的目的:
- 使用ML算法预测困难的输管.
- 评估人体测量和超声波气道测量的有效性.
- 为了提高术前呼吸道评估的准确性.
主要方法:
- 一项前性观察性研究包括329名接受全身麻醉的成年患者 (ASA I-III).
- 收集的手术前数据包括修改的Mallampati分数,人体测量测量和各种超声波气道尺寸.
- 八个ML算法被用来分析数据集以预测性能.
主要成果:
- 支持矢量机 (SVM) 算法在预测难以输管的过程中表现出最高的准确性 (89.39%).
- 确定的关键预测因素是修改的马兰帕蒂分数,子周长,皮肤到皮质膜间的距离和舌头厚度.
- 该模型的负预测值为92.7%,正预测值为72.7%.
结论:
- 对于难以输管的传统床边测试具有有限的预测能力.
- 将超声波数据与ML模型集成,与单独的临床评估相比,显著提高了预测准确性.
- 集成这种ML模型的移动应用程序可以为手术前的气道评估提供快速,客观的工具,提高患者的安全.
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