一个云模型和数据包裹分析增强了FMEA的风险评估框架,用于3D打印模板辅助基疗法
Ran Luo1,2, Jiuling Shen3, Lian Duan4
1Department of Radiation Oncology, West China Hospital, Sichuan University, Chengdu, China.
Medical physics
|January 24, 2026
概括
结合云模型和数据包裹分析 (DEA) 的新混合框架改善了对3D打印模板辅助内腔/间隙支架疗法 (3DP-IC/IS) 的风险评估. 这种增强的故障模式和效应分析 (FMEA) 更好地识别了关键故障模式,改善了复杂放射治疗的质量保证.
科学领域:
- 放射治疗和医学物理学的研究.
- 风险管理和质量保证
- 计算建模和数据分析数据分析
背景情况:
- 传统的故障模式和影响分析 (FMEA) 是放射治疗中常见的风险评估工具.
- 3D打印模板辅助内腔/间隙支臂疗法 (3DP-IC/IS) 的复杂性需要先进的风险管理.
- 现有的FMEA方法具有需要解决的数学和逻辑限制.
研究的目的:
- 开发一个先进的FMEA框架,整合云模型和数据包裹分析 (DEA).
- 为了验证这种增强的FMEA框架的性能与3DP-IC/IS.中的传统FMEA相比.
- 改进复杂放射治疗程序中故障模式的识别和优先级.
主要方法:
- 开发了一个混合框架,结合了语言评估模糊性的云模型和多标准决策的修改后的DEA.
- 该框架应用于80名接受3DP-IC/IS.患者的数据集.
- 故障模式 (FMs) 的排名是使用建议的和传统的FMEA方法进行比较分析的.
主要成果:
- 加强的FMEA将"不充分检查针标签"和"过多的针头"确定为高风险的FM.
- 该框架改善了风险排名歧视,减少了基于分数的分组.
- 四个FMEA被重新归类为高风险,表明与传统FMEA相比,对潜在的低估风险的敏感性更大.
结论:
- 混合云模型-DEA-FMEA框架有效地解决了传统风险评估的局限性.
- 该框架是可行的,并增强了3DP-IC/IS.中的关键FM的识别和优先级.
- 这种方法在复杂的放射治疗中为质量保证和患者安全提供了重要的临床价值.
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