在放射治疗中深度学习细分的性能监测和持续质量保证的统计过程控制
Niels van Acht1,2, Dave van Gruijthuijsen1, Johanna Bluemink1
1Department of Radiation Oncology, Catharina Hospital, Eindhoven, the Netherlands.
Physics and imaging in radiation oncology
|December 16, 2025
概括
实施了新的持续质量保证 (CQA) 框架,用于放射治疗中的深度学习细分 (DLS). 该系统使用统计过程控制和纳尔逊规则自动检测DLS模型中的异常值和性能偏差.
科学领域:
- 放射治疗和医学成像技术
- 医疗保健中的人工智能
- 医疗技术的质量保证 医疗技术的质量保证
背景情况:
- 深度学习细分 (DLS) 的临床实施需要常规质量保证 (QA).
- 欧盟人工智能法规要求记录DLS模型输出以进行持续质量保证 (CQA).
- 监测DLS性能和检测偏差对于患者安全至关重要.
研究的目的:
- 实施一个CQA框架,专门用于放射治疗中的DLS.
- 实现实现后的DLS性能自动监控.
- 确保在医疗保健中遵守新兴的人工智能法规.
主要方法:
- 自动导出DLS输出和临床细分 (CS).
- 对感兴趣地区 (ROI) 的几何指标的计算.
- 应用统计过程控制 (SPC) 和适应的纳尔逊规则用于异常值和趋势转移的检测.
主要成果:
- 在六个月内分析了500多个DLS/CS文件和3000个ROI.
- 3,0%的ROI自动标记为异常值.
- 检测到四个趋势变化,包括部细分的性能下降,以及其他十二个趋势变化和一个漂移.
结论:
- 在放射治疗中成功建立了DLS的CQA框架.
- 该框架使用SPC和Nelson规则来自动检测异常值和趋势变化.
- 这种实现有助于持续监控和维护DLS性能.
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