使用非侵入性参数构建双AI模型和名图,以帮助诊断男性膀出口阻塞,并尽量减少对侵入性视频-尿动力学研究的需求:开发和验证研究研究
Chung-You Tsai1,2, Jing-Hui Tian3, Chien-Cheng Lee2
1Divisions of Urology, Department of Surgery, Far Eastern Memorial Hospital, New Taipei, Taiwan.
Journal of medical Internet research
|July 23, 2024
概括
机器学习模型可以使用非侵入性临床数据预测男性的膀出口阻塞 (BOO). 这种方法可以减少对视频尿动力学研究 (VUDS) 等侵入性诊断程序的需求.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 由于膀出口阻塞 (BOO),诊断男性下尿道症状 (LUTS) 是一个挑战.
- 通常需要进行诸如视频尿动力学研究 (VUDS) 和压力流量研究 (PFS) 等侵入性测试.
- VUDS为男性BOO提供了比PFS更精确的病因差异化.
研究的目的:
- 开发用于BOO诊断的非侵入性预测模型.
- 为了减少LUTS.男性的侵入性诊断程序的必要性.
- 为临床医生创建一个决策支持工具.
主要方法:
- 对经过VUDS的怀疑BOO的男性进行了回顾性研究.
- 开发了两种预测模型:国际抑郁协会定义的BOO (ICS-BOO) 和视频尿动力学研究诊断的BOO (VBOO).
- 使用了6个机器学习算法,并使用交叉验证和独立测试集进行了验证.
主要成果:
- 在测试验证中,ICS-BOO模型获得了0.86的AUC;在外部测试中,VBOO模型获得了0.72的AUC.
- 采用双模型方法可以在32.6%的患者中潜在地避免VUDS.
- 组合模型预测增强了临床决策和诊断途径的简化.
结论:
- 两种机器学习模型使用非侵入性参数有效预测ICS-BOO和VBOO.
- 双模型预测策略可以帮助避免VUDS,当两种模型都积极预测BOO时.
- 这种方法有助于男性BOO诊断,并减少对侵入性测试的依赖.
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