气道管理中的人工智能:系统性审查和元分析
Mohamed F Abosamak1, Hany A Zaki2, Eman E Shaban3
1Department of Anesthesia and Intensive Care Medicine, Faculty of Medicine, Tanta University, Egypt; Department of Adults ICU, Security Forces Hospitals Program, General Directorate of Medical Services, Ministry of Interior, Riyadh, Saudi Arabia.
Anaesthesia, critical care & pain medicine
|July 11, 2025
概括
人工智能 (AI) 在预测困难的呼吸道方面表现有前途,但大多数模型需要改进. 进一步开发和验证对于提高麻醉患者的安全性至关重要.
科学领域:
- 麻醉学和重症监护医学
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 呼吸道管理在麻醉中至关重要;困难的呼吸道带来致命的风险.
- 人工智能 (AI) 具有改善临床性能的潜力.
- 这篇评论综合了人工智能对难以预测呼吸道的证据.
研究的目的:
- 总结当前关于人工智能模型在预测困难气道方面的有效性证据.
- 为难以预测气道的AI模型确定表现最好的AI模型.
主要方法:
- 在PubMed和Science Direct上进行了系统的文献搜索,直到2025年3月.
- 使用R软件进行了接收器操作曲线下的面积 (AUROC) 的元分析.
- 13项研究符合审查的纳入标准.
主要成果:
- 人工智能模型是为急诊室 (ED) 和全身麻醉患者开发的.
- 深度学习模型VGG表现出最高的区分能力 (AUC 0.84).
- 支持向量机 (SVM) 和天真贝叶斯 (NB) 等传统模型显示出良好的区分能力 (AUC分别为0.80和0.81).
结论:
- 一些人工智能模型对困难的气道表现出良好的区分能力 (AUC ≥ 0.80).
- 然而,许多人工智能模型仅显示平均性能 (AUC < 0.80).
- 显然需要开发更准确的AI模型并验证现有的模型.
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