在冠状动脉CT图像上检测冠状动脉狭窄的端到端深度学习模型
Vibha Gupta1, Petur Petursson2, Aidin Rawshani3
1Department of Molecular and Clinical Medicine, University of Gothenburg Institute of Medicine, Gothenburg, Sweden gupta85vibha19085@gmail.com.
Open heart
|January 12, 2025
概括
深度学习模型在CCTA扫描中准确地检测冠状动脉狭窄. EfficientNet模型显示出高诊断能力,用于检测左前下降动脉中中度和重度狭窄.
科学领域:
- 心血管成像 - 心血管成像
- 人工智能在医学中的应用
- 深度学习用于医学诊断
背景情况:
- 检测冠状动脉狭窄对于心血管疾病管理至关重要.
- 冠状动脉CT血管造影 (CCTA) 是评估冠状动脉的关键成像方式.
- 深度学习为复杂的医学成像数据的自动分析提供了潜力.
研究的目的:
- 评估端到端的深度学习模型来检测中度 (≥50%) 或重度 (≥70%) 狭窄.
- 评估模型在左前下降 (LAD),右冠状动脉 (RCA) 和左环动脉 (LCX) 的性能.
- 通过热图可视化研究深度学习模型的可解释性.
主要方法:
- 从CCTA扫描中开发端到端深度学习模型,使用曲线多平面改造 (CMR).
- 利用了使用EfficientNet,ResNet,DenseNet和Inception-ResNet架构的转移学习.
- 采用了通过交叉验证优化的类加权策略,并生成了可解释性的热图.
主要成果:
- EfficientNet模型表现出卓越的性能,特定的模型在LAD,RCA和LCX方面表现出色.
- 获得高的接收器操作特征曲线 (AUROC) 下面面积值:LAD中度为0.95,LAD重度为0.94,RCA中度/重度为0.92.
- 热图证实了模型在识别狭窄性病变方面的准确性;决策曲线分析支持了模型的有效性.
结论:
- 端到端深度学习模型,特别是EfficientNet,显示出对LAD狭窄检测的优异区分能力.
- 这些模型提供了精确且高度可解释的结果,有助于临床决策.
- 内部验证证实了深度学习在冠状动脉狭窄评估中的诊断能力.
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