深度学习算法预测差异性功能<40%的单侧水缩基于尿路超声波的关键参数
Jiaxin Huang1, Liyuan Geng2, Yue Hu3
1Department of Urology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.
Urology
|April 10, 2025
概括
尿路超声波可以预测功能差异 (DRF) 在患有水性的儿童中<40%. 使用超声波结果和年龄的支持矢量机器 (SVM) 模型显示,它在识别需要手术的儿童方面具有很高的准确性.
科学领域:
- 儿科脏病学 儿科脏病学
- 医疗成像医学成像
- 机器学习在医学中的应用
背景情况:
- 儿童单侧水缩通常需要手术干预,当功能差异 (DRF) <40%.
- 尿液瘤学是评估DRF的标准,但并不总是可以获得的.
- 尿路超声波在这些情况下为评估功能提供了一个非侵入性的替代方案.
研究的目的:
- 为了确定超声波参数和DRF<40%在儿科单侧水中之间的相关性.
- 用超声波数据评估机器学习模型在预测功能障碍方面的实用性.
- 根据非侵入性成像识别高风险患者进行手术干预.
主要方法:
- 对802名患有水缩症的儿童的超声波和光学数据进行了回顾性分析.
- 使用统计查,确定DRF<40%的独立风险因素.
- 使用关键超声波参数和患者年龄,开发和比较机器学习模型 (随机森林,物流回归,SVM).
主要成果:
- 骨盆前后直径,上扩张和长度比是DRF<40%的显著风险因素.
- 长度比率显示,在单个因素中,接受操作曲线下的面积 (AUROC) 是0.820的最高值.
- 结合这些因素和年龄,SVM模型实现了0.941的AUROC,灵敏度为90.32%,特异性为81.03%.
结论:
- 长度比是儿童水缩症中DRF<40%的强烈超声波预测因素.
- 与个人超声波指数相比,机器学习,特别是SVM显著提高了预测准确性.
- 这种SVM模型可以有效地识别患有先天性水缩症的儿童,这些儿童面临功能障碍的高风险,有助于进行手术决策.
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