两个新的深度学习模型来预测自发的尿路结石通过:模型开发和验证
Zhiying Xiao1, Hui Bai2, Yumeng Zhang1
1Department of Urology, The Second Hospital of Shandong University, Jinan, China.
Current urology
|April 21, 2025
概括
深度学习模型有效地预测了自发性尿道结石通道 (SSP). 3D ResNet29模型显示出卓越的准确性,帮助临床决定手术干预与尿道结石的保守管理.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 尿道结石影响了大量患者.
- 预测自发石头通过对于治疗规划至关重要.
- 当前的方法,如石头大小,有局限性.
研究的目的:
- 开发和评估深度学习 (DL) 模型,用于预测自发性尿道结石通道 (SSP).
- 将DL模型的性能与传统方法进行比较.
主要方法:
- 对1217名尿道结石患者的回顾性分析.
- 开发了两个DL模型:二维ResNet29和三维ResNet29.
- 与基于石材直径 (<5毫米) 的传统方法进行比较.
主要成果:
- 3D ResNet29模型实现了90.6%的精度,88.2%的灵敏度和95.1%的特异性.
- 无论是2D还是3DDL模型,都显著超过了石头直径方法.
- 在ROC分析中,DL模型显示曲线下面的面积更大.
结论:
- 深度学习模型,特别是3D ResNet29,对于预测SSP是非常有效的.
- 这些模型可以帮助临床医生在外科干预和保守管理之间做出决定.
- 新的DL方法为尿道结石的结果提供了更好的预测准确性.
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