基于人工智能 (AI) 的多器官轮质量保证与在线自适应性放射治疗 (oART) 的不确定性估计
Shunyu Yan1, Jiacheng Xie1, Na Chen1
1The Medical Artificial Intelligence and Automation (MAIA) Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America.
Machine Learning. Health
|January 21, 2026
概括
本研究介绍了用于自动化放射治疗轮质量保证 (QA) 的AI框架. ConQuE模型有效地识别了高准确度和量化的不确定性所需的修订,这对于在线适应性RT至关重要.
科学领域:
- 医学物理与成像学
- 医疗保健中的人工智能
- 放射治疗技术 放射治疗技术
背景情况:
- 精确划分有风险的器官 (OARs) 和治疗目标对于有效的放射治疗 (RT) 来说至关重要.
- 当前的人工智能细分方法可以自动划分,但缺乏强大的质量保证 (QA) 机制,特别是对于时间敏感的在线自适应RT (oART).
- 一个可靠和高效的质量保证过程对于确保人工智能驱动的RT工作流程的安全性和有效性至关重要.
研究的目的:
- 开发和验证一个人工智能驱动的框架,用于自动化的多器官轮质量保证 (QA),以不确定性量化.
- 为解决在前列腺癌MR导向在线自适应性放射治疗 (oART) 的背景下对高效可靠的轮质量保证的未满足的临床需求.
- 评估拟议框架的准确性,效率和可靠性,以确定需要修订的细分.
主要方法:
- 开发了一种使用ResNet34骨干的轮质量估计 (ConQuE) 模型,以将细分面罩分类为"可接受"或"需要修订".
- 通过将结构特定的信息整合到完全连接的层面,实现了多机构质量保证的统一框架.
- 雇员蒙特卡洛 (MC) 放弃不确定性量化,使得对临床决策预测的信心评估.
主要成果:
- 康奎模型在多个结构 (前列腺,直肠,膀,尿道,阴茎球,马尾巴,股骨头) 中实现了93.9%的整体准确性,结构特异性性能始终在90%以上.
- 该框架表现出高效率,在大约13.1毫秒内完成了一张图像片的质量保证,并通过了30个MC掉落通道.
- 错误的预测与高不确定性得分有很强的相关性,这表明该模型在标记潜在错误轮方面的可靠性.
结论:
- 拟议的基于AI的框架自动化了具有量化不确定性的轮QA,为放射治疗提供了可靠的解决方案.
- 高精度,效率和可靠性使该框架特别适合时间有限的oART环境.
- 这项技术有可能显著提高自适应性放射治疗工作流程的安全性和效率.
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