通过机器学习方法从患者手术前照片中可靠地预测气管输管管道难度
Fernando García-García1, Dae-Jin Lee2, Francisco J Mendoza-Garcés3
1Basque Center for Applied Mathematics (BCAM) - Bilbao (Basque Country), Spain.
Computer methods and programs in biomedicine
|March 15, 2024
概括
机器学习模型使用气道形态特征准确地预测难以进行的气管管道输入. 这些模型显著超过了专家判断和现有方法,改善了麻醉规划和患者安全.
科学领域:
- 麻醉学 麻醉学
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 估计困难的气管管道输液风险对于麻醉规划和患者安全至关重要.
- 目前用于输管困难的床边查具有较低的灵敏度.
研究的目的:
- 开发和评估机器学习 (ML) 和深度学习 (DL) 算法,以利用气道形态学预测输管风险.
- 将开发的算法的性能与专家判断和最先进的方法进行比较.
主要方法:
- 研究人员对623名接受气管管道输液的患者进行了前性队列研究.
- 来自手术前照片的2D解剖学地标被用于导出3D坐标并计算59个形态特征.
- 开发了四个ML管道 (物流回归,SVM,随机森林,XGBoost),结合特征缩放,归算,重新抽样和分类,使用形态和人口统计数据.
- 还开发了一个特设的多输入深卷积神经网络 (DCNN) 来直接从图像中预测输管风险.
主要成果:
- 使用工程形态特征的四个ML管道实现了0.746和0.766之间的中位数AUC,超过了专家判断 (AUC0.716) 和最先进的方法.
- 多输入的DCNN和最先进的DL算法由于过度装配而表现不佳.
- 在最佳贝叶斯决策值下,XGBoost管道表现出最好的表现,错误负数最少.
结论:
- 经过验证的ML模型可以可靠地估计气道输管风险,协助临床医生进行麻醉规划.
- 这一新的工程形态特征集为预后提供了信息性的描述.
- 开发的ML模型超越了专家评估和当前最先进的方法来预测输管困难.
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