在使用深度学习的序列CT成像上对结石负担的全自动纵向评估
Pritam Mukherjee1, Sungwon Lee1, Daniel C Elton1
1Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Maryland, USA.
Journal of endourology
|June 13, 2023
概括
深度学习 (DL) 准确地自动化了CT扫描上的结石负担测量. 这项技术与手动评估具有很高的一致性,改善了尿病的跟踪.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 结石 (结石) 管理需要精确监测石头负担.
- 连续CT扫描用于跟踪石块体积随时间变化.
- 手动测量结石负担可能耗时,并且受观察者之间的变化影响.
研究的目的:
- 开发和验证深度学习 (DL) 模型,用于自动测量和跟踪结石负担.
- 评估基于DL的自动测量与手动评估石头负荷及其在串行CT扫描上的变化之间的一致性.
主要方法:
- 一项回顾性研究包括259个CT扫描来自113名患有尿病的患者.
- 在初始和后续扫描中使用DL模型检测,细分和测量结石体积 (SV).
- 石头负荷 (SV) 和其变化 (SVA,SVR) 的自动测量与使用一致性相关系数 (CCC) 和布兰德-阿尔特曼图表的手动评估进行了比较.
主要成果:
- 对于石头检测,DL管道实现了高每扫描灵敏度 (97.8%) 和正预测值 (96.6%).
- 在石块体积的自动和手动测量 (CCC=0.995),绝对变化 (CCC=0.980) 和相对变化 (CCC=0.915) 之间发现了很好的一致性.
- DL模型有效量化了石头负荷及其间隔变化,证明了高可靠性.
结论:
- 基于DL的自动测量可以在串行CT扫描上准确可靠地评估结石负担.
- DL技术提供了一个有前途的工具,可以简化和提高尿病监测的客观性.
- 这种方法有可能在治疗结石方面加强临床决策.
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