门定制如何影响初级保健分类的多类X射线AI模型的性能:一项回顾性研究
Jordan Zheng Ting Sim1, Jia Lin2, Qi Wei Fong3
1Diagnostic Radiology, Tan Tock Seng Hospital, Singapore, Singapore jordan.zt.sim@nhghealth.com.sg.
BMJ open
|February 14, 2026
概括
优化用于胸部X射线 (CXR) 分析的人工智能 (AI) 模型的操作值对于初级保健分组至关重要. 调整值可以提高灵敏度和负预测值,支持安全的人工智能辅助工作流程.
科学领域:
- 医疗成像中的人工智能
- 放射学工作流的优化 工作流的优化
- 深度学习用于胸部X射线分析
背景情况:
- 商用深度学习 (DL) 模型用于多类胸部X射线 (CXR) 分析,需要适应特定的临床环境.
- 初级保健机构需要人工智能工具,优先考虑安全,尽量减少虚假负面.
研究的目的:
- 描述商业CXR DL模型的值优化过程.
- 在各种值下评估模型的诊断性能.
- 估计人工智能支持的初级保健分组对潜在的运营影响.
主要方法:
- 对多类CXR DL模型的追溯诊断性能评估.
- 基于值的分析,使用来自新加坡初级保健诊所和三级医院的数据.
- 包括816个成人前部CXR,但不包括儿科研究和非标准视图.
主要成果:
- 在0.10的值时,灵敏度增加到93.2%,负预测值 (NPV) 增加到91.3%,特异性为71.7%.
- 将值从0.15降至0.10提高了灵敏度和NPV,这对于以安全为重点的分类工作流程是可以接受的.
- 该权衡优先考虑尽量减少虚假阴性,这对初级保健安全至关重要.
结论:
- 值优化对于将人工智能模型定制为临床工作流程至关重要.
- 调整操作值可以优先考虑灵敏度和NPV,以实现安全的人工智能辅助分组.
- 放射学和临床团队之间的协作努力对于在初级保健中有效实施人工智能至关重要.
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