在临床实践中,基于穆雷定律的定量流量比率能力:验证常规诊断性能和分析学习曲线值
Dan Deng1,2, Yue Feng1,2, Min Zeng1,2
1Department of Cardiovascular Medicine, Center for Circadian Metabolism and Cardiovascular Disease, Southwest Hospital Army Medical University Chongqing China.
Journal of the American Heart Association
|December 11, 2025
概括
操作员的经验会影响AI驱动的定量流量比率 (QFR) 的准确性. 对于可靠的QFR,至少需要170艘船只,峰值性能约为441例. 结构化培训对于临床整合至关重要.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 第二代基于穆雷定律的定量流量比 (QFR) 使用人工智能改进血管学衍生生理学评估.
- 在常规实践中,人工智能驱动的QFR的诊断可靠性受操作员学习曲线的影响.
研究的目的:
- 评估操作员学习曲线对基于穆雷定律的AI驱动QFR诊断可靠性的影响.
- 确定实现高诊断性能和稳定性所需的病例数.
主要方法:
- 连续的怀疑心肌缺血的患者接受了AI驱动的QFR和分数流量储备测量.
- 通过接收器操作特征分析评估诊断准确性,并通过顺序数据扩展分析学习曲线.
- 诊断性能的稳定性通过计算第一阶段AUC差异来评估.
主要成果:
- 人工智能驱动的QFR显示出出色的诊断准确性,血管水平AUC为0.92和患者水平AUC为0.91.
- 在分析了170个血管后,获得了高诊断性能 (AUC ≥0.90).
- 峰值性能和稳定性在441艘船只中达到,增量AUC值没有显著偏差.
结论:
- 操作员的经验极大地影响了基于穆雷定律的AI驱动的QFR的准确性.
- 至少分析了170艘船只,确保高QFR可靠性,在441个案例中提供最佳性能.
- 结构化培训对于人工智能增强的冠状动脉生理学工具的有效临床整合至关重要.
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