一个基于超声波的人工智能框架,用于困难的气道预测:一个两模型,三步决策框架框架.
Chunmeng Fu1, Cunyuan Luan2, Huabo Liu2,3
1Department of Anesthesiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
PloS one
|February 18, 2026
概括
这项研究介绍了一种使用超声波来难以预测通气道的AI框架. 开发的模型在识别有风险的患者,帮助临床决策方面表现有前途.
科学领域:
- 麻醉学 麻醉学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 难以管理的气道 presents显著的临床挑战.
- 超声波参数和深度学习显示了难以评估气道的潜力.
- 需要一个人工智能框架来进行困难的气道预测.
研究的目的:
- 开发和内部验证基于超声波的AI框架,用于预测困难的气道.
- 为风险分层构建一个"两种模式,三步"的层次战略.
- 在困难的气道预测中评估AI模型的性能.
主要方法:
- 研究了一组903名接受选择性全身麻醉的患者.
- 使用部超声波图像开发了两种卷积神经网络模型,即CL-AI和VIDIAC-AI.
- 模型性能使用交叉验证和独立测试集进行了评估.
主要成果:
- 困难的喉腔镜检查发生在20.9% (直接) 和5.5% (视频) 的患者中.
- 该CL-AI模型的AUC值为0.86,准确度为0.84.
- 在VIDIAC-AI模型中,AUC值为0.82,准确度为0.81.
结论:
- 一个基于超声波的AI框架被开发用于困难的气道风险分层.
- "两个模型,三个步骤"框架作为临床决策支持工具.
- 需要在大型多中心队列中进一步验证.
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