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使用深度学习和多器官细分的胸部CT完全自动化的冠状动脉分数和风险分类:来自国家肺部查试验 (NLST) 的验证研究

Sudhir Rathore^1,2, Ashish Gautam³, Prashant Raghav³

¹Department of Cardiology, Frimley Park Hospital NHS Foundation Trust, Camberley, Surrey, UK.

International journal of cardiology. Heart & vasculature

|January 24, 2025

概括

一个深度学习模型从胸部CT扫描中准确地检测和分类冠状动脉化得分 (CACS). 这种自动化方法有助于评估高风险肺癌查患者的心脏病风险.

科学领域:

放射学放射学是一门学科.
人工智能的人工智能
心脏病学心脏病学

背景情况:

低剂量CT的肺癌查可以识别高风险个体.
这些患者还面临冠状动脉疾病的风险增加.
冠状动脉化评分 (CACS) 是心血管风险的一个关键指标.

研究的目的:

开发和验证用于自动化CACS检测和风险分层的深度学习模型.
为了评估模型的性能与手动评估和地面真相相对比.
评估CACS自动化分析在临床实践中的潜力.

主要方法:

使用Mask R-CNN的神经网络用于自动心脏和大动脉细分和量化.
该模型对80名患者进行了训练,并对1442名患者进行了验证.
性能与使用专有软件的手动量化进行了比较.

主要成果:

自动化细分模型实现了0.91.9的高平均子得分.
该模型与参考标准有很强的一致性,风险类别的科恩卡帕为0.72.
它在78.8%的病例中正确地分类了风险组,在检测冠状动脉化时具有高灵敏度 (90%) 和特异性 (94%).

结论:

用于CACS分析的全自动深度学习模型显示出与参考标准的良好相关性.
自动化可以提高诊断能力和冠状动脉疾病的风险分层.
这种方法可以促进早期干预,改善患者的治疗结果,并降低医疗保健成本.

关键词:

人工智能的人工智能胸部CT CT 的情况.冠状动脉分数冠状动脉分数冠状动脉疾病是一种冠状动脉疾病.风险分类风险分类.

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