线性加速器 (linac) 停机时间分析辅助大型语言模型 (LLM).
Yeochan Youn1,2, Joseph B Schulz3, Ethan Stolen2
1Data Science Institute, University of Chicago, Chicago, Illinois, USA.
Journal of applied clinical medical physics
|November 27, 2025
概括
这项研究分析了线性加速器 (LINAC) 维护记录,发现了一致的正常运行时间,并确定了需要维修的关键组件. 这些发现为优化放射治疗维护和预算管理提供了见解.
科学领域:
- 医学物理 医学物理
- 辐射瘤学 辐射瘤学
- 生物医学工程 生物医学工程
背景情况:
- 线性加速器 (LINAC) 对于放射治疗至关重要,对患者的护理要求高可靠性.
- 在LINAC中计划外停机会破坏临床工作流程,并可能损害治疗效率.
- 传统的可靠性分析往往忽略了全面的性能模式,而偏好了特定组件的问题.
研究的目的:
- 分析来自Varian TrueBeam LINAC的10年维护记录,以了解长期的运行性能.
- 为了识别常见的故障模式,量化停机时间,确定脆弱的组件,并评估大流行对维护的影响.
- 利用人工智能和大型语言模型 (LLM) 来分析非结构化维护数据.
主要方法:
- 分析了三台Varian TrueBeam LINAC在4至13年的运行中1584份维护报告.
- 采用数据规范化来标准化服务文档和基于LLM的定制提示工程来进行报告分类 (工作和故障类型).
- 进行描述性分析,包括季节性趋势的时间序列分解和外部技术人员参与的评估.
主要成果:
- 利纳克公司在商定的5%门内保持了运营停机时间,只有一个案例接近限额.
- 结合系统,控制硬件和电源系统是最常见的维护主题;维修和更换是常见的工作类型.
- 没有观察到与年龄相关的故障频率增加;检测到微弱的季节性. 现场服务工程师的访问和2020年后的工作时间增加了,特别是替换人员.
结论:
- 研究结果为临床预算管理和LINAC维护计划提供了宝贵的见解.
- 开发的基于LLM的数据分析方法为分析历史维护记录提供了有效的方法.
- 其他机构可以采用这种方法来提高LINAC的运营性能和维护策略.
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