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基于深度学习的冠状动脉和左心室的分类,使用多模式数据进行自主协议选择或血管学调整

Arpitha Ravi¹, Philipp Bernhardt², Mathis Hoffmann²

¹Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), 91058, Erlangen, Germany. arpitha.ravi@fau.de.

Scientific reports

|April 30, 2025

概括

一个新的深度学习模型从X射线图像中自动识别心脏解剖学,为冠状动脉血管学等手术提供了优化的成像参数. 这种人工智能方法提高了图像质量,并减少了患者的辐射暴露.

科学领域:

医疗成像医学成像
人工智能的人工智能
心脏病学心脏病学

背景情况:

最佳的X射线成像参数对于冠状动脉血管学和结构性心脏手术至关重要,平衡图像质量和辐射剂量.
手动选择取决于解剖学的成像参数增加了临床工作量和复杂性.

研究的目的:

开发和评估一个深度学习算法,从单个X射线中自主检测心脏解剖 (左冠状动脉,右冠状动脉,左心室).
通过根据检测到的解剖学选择适当的器官程序来实现成像参数的自动调整.

主要方法:

通过使用275个临床放射学序列 (冠状动脉血管学,左心室内膜学) 的数据集,比较了三种深度学习架构 (ResNet-50,MLP,多模式).
利用了第一个非对比框架用于解剖学检测,允许对比前系统适应.
在一个由146个序列组成的独立测试集上评估模型.

主要成果:

多模式深度学习模型的平均F1得分为0.82和AUC为0.87,与专家的表现相匹配.
该模型准确地从没有可见结构的前对比中分类心脏解剖学.
证明了对心脏解剖学的有效预测,用于电影收购.

结论:

拟议的深度学习模型准确预测心脏解剖学,促进自动选择成像参数.
这种人工智能驱动的方法有可能优化图像质量,减少辐射暴露,简化工作流程,提高心脏手术中的患者安全.

关键词:

心脏解剖学的分类心脏解剖学分类深度学习是一种深度学习.对X射线成像参数优化进行了优化.

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