多实例学习与注意力机制用于冠状动脉狭窄检测冠状动脉计算机断层扫描血管学
Vibha Gupta1,2,3, Petur Petursson4, Lukas Hilgendorf1,3
1Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Bruna stråket 16, 413 45 Göteborg, Gothenburg, Sweden.
European heart journal. Digital health
|May 21, 2025
概括
深度学习模型使用多实例学习和注意力得分准确检测冠状动脉狭窄 (CAS). 这种方法提高了冠状动脉计算机断层扫描血管造影的诊断精度,改善了患者的治疗结果.
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:
- 准确检测冠状动脉狭窄症 (CAS) 对于预防心脏事件至关重要.
- 在CAS检测的挑战包括数据复杂性和成像协议在计算机断层扫描血管学变化.
- 深度学习 (DL) 对自动化CAS检测有希望,但需要强大的方法来实现现实应用.
研究的目的:
- 开发和评估一个多实例学习 (MIL) 模型,用于自动检测显著的CAS (≥50%光阻塞).
- 评估模型在左前下降 (LAD),右冠状动脉 (RCA) 和左环动脉 (LCX) 中识别CAS的性能.
- 调查注意力评分对提高诊断解释性和精度的有用性.
主要方法:
- 总共有900例预生成的曲线多平面改造被用于培训.
- 训练了一种多实例学习 (MIL) 模型,以检测LAD,RCA和LCX中的显著CAS.
- 用注意力得分来衡量个别图像片的贡献,得出了患者一级的预测.
主要成果:
- 在MIL模型中,LAD (AUC:0.92),RCA (AUC:0.91) 和LCX (AUC:0.92) 的高性能得到证明.
- 注意力得分有效地确定了最相关的图像切片,提高了诊断精度.
- 模型校准对于LAD很好,对RCA和LCX观察到的精度较低.
结论:
- 多实例学习模型显示了CAS检测的巨大潜力,特别是在LAD动脉中.
- 使用注意力得分可以有效识别关键的图像数据,并无地集成到临床工作流程中.
- 开发的方法解决了数据的复杂性,提高了诊断准确性,支持冠状动脉疾病评估中的临床决策.
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