通过非对比增强计算机断层扫描图像自动检测尿路结石
Juncheol Lee1, Dong-Hyun Jang2,3, Young-Jin Jeon4
1Department of Emergency Medicine, College of Medicine, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea.
Urolithiasis
|December 8, 2025
概括
一个新的深度学习模型UROAID使用计算机断层扫描精确检测尿路结石. 这种人工智能系统有助于区分石头和化,改善在紧急情况下的诊断.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
背景情况:
- 尿路结石会引起严重的疼痛和阻塞,往往需要紧急护理.
- 在CT扫描上区分尿路结石与血管化或结石对临床医生来说可能是个挑战.
研究的目的:
- 实施和评估UROAID (UROlothiasis AssIsted Diagnosis系统) 深度学习模型,用于检测尿路结石.
- 为了提高尿路结石诊断在紧急情况下的准确性和效率.
主要方法:
- 使用了6659名成年患者的非对比腹腔皮层CT扫描.
- 开发了UROAID,这是一个组合模型,结合了细分 (修改的Uro-UNETR) 和分类模块.
- 集成的ROI提取,KUB细分和尿道系统估计模块,用于全面检测石头.
主要成果:
- 实现了高性能,精度为0.9585和F1得分为0.9605.5.
- 在尿路位置显示出优异的检测率:脏 (99.0%),近端尿道 (99.1%),中部尿道 (98.0%),远端尿道 (96.4%),尿 (91.3%).
结论:
- UROAID有效检测尿路结石,模仿放射科医生的诊断过程.
- 拟议的整体模型提高了尿路结石的诊断性能.
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