基于深度学习的冠状动脉细分和X射线冠状动脉血管学中的狭窄检测X射线冠状动脉血管学
Mitchel A Molenaar1, Elsa Hebbo2, Jasper L Selder1
1Amsterdam UMC Heart Centre, Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands.
JACC. Advances
|December 25, 2025
概括
深度学习模型现在可以自动分割冠状动脉,并在侵入性冠状动脉血管学 (ICA) 图像上检测显著的狭窄. 这些人工智能工具的性能与人类专家相美,在心脏诊断方面提供了有前途的进步.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 深度学习 (DL) 显示了在侵入性冠状动脉血管学 (ICA) 中自动化冠状动脉检测的潜力.
- 准确的细分和狭窄检测对于诊断冠状动脉疾病至关重要.
研究的目的:
- 在ICA上开发和验证DL模型用于冠状动脉细分和显著狭窄检测.
- 将这些DL模型的性能与专家可变性进行比较.
主要方法:
- 来自两个主要医疗中心的ICA研究的回顾性收集.
- 冠状动脉和狭窄的手动细分由专家.
- 使用单中心和多中心数据集训练DL模型.
- 外部验证和与专家绩效的比较.
主要成果:
- 一组合的数据集包括10573张ICA图像,用于模型培训.
- 在两个中心的数据上训练的DL模型实现了最高的细分性能 (迪斯系数:0.86).
- 通过DL模型检测狭窄的检测率 (0.67) 与专家一致意见 (0.65) 相比.
结论:
- DL模型在ICA上的冠状动脉细分方面展示了专家级别的性能.
- 由人工智能驱动的工具可以有效地检测主要冠状动脉中的显著狭窄.
- 这些发现支持将DL整合到常规心脏成像工作流程中.
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