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深度学习和放射学使用CTA对冠状动脉慢性全闭和亚全闭的歧视

Zhen Zhou¹, Kairui Bo¹, Yifeng Gao¹

¹Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China (Z.Z., K.B., Y.G., H.Z., Y.C., Y.C., H.W., N.Z., L.X.).

Academic radiology

|March 31, 2025

概括

深度学习和放射学模型使用冠状动脉计算机断层扫描血管学 (CCTA) 准确地区分冠状动脉慢性全封闭 (CTO) 和亚全封闭 (STO). 这些人工智能驱动的方法与诊断这些具有挑战性的冠状动脉病变的传统方法相比,显示出更高的性能.

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:

慢性冠状动脉全封闭 (CTO) 和亚全封闭 (STO) 存在诊断困难,需要不同的治疗策略.
准确区分CTO和STO对于有效的患者管理至关重要.
人工智能 (AI) 和放射学为改善诊断准确性提供了潜在的解决方案.

研究的目的:

开发和验证深度学习 (DL) 和放射学模型,以使用冠状动脉计算机断层扫描血管学 (CCTA) 来区分CTO和STO.
将这些基于AI的模型的诊断性能与传统方法进行比较.

主要方法:

从第三级医院追溯识别CTO和STO病变,用于模型培训和验证.
使用CCTA数据开发DL和放射学模型.
验证使用两个额外的三级医院的外部队列.
与传统的诊断方法进行比较.

主要成果:

DL模型实现了0.908 (验证) 和0.893 (外部测试) 的曲线下面积 (AUC).
放射学模型的AUC值为0.860 (验证) 和0.827 (外部测试).
无论是DL还是放射学模型,都显著超过了传统方法 (AUC 0.794验证,0.746外部测试).

结论:

基于CCTA的DL和放射学模型提供了冠状动脉CTO和STO之间的高效和准确的区分.
这些人工智能驱动的方法在诊断具有挑战性的冠状动脉病变方面取得了重大进展.

关键词:

计算机断层扫描血管学冠状动脉慢性全闭塞冠状动脉全闭塞冠状动脉次总闭塞冠状动脉次总闭塞深度学习 (Deep Learning) 是一种深度学习.无线电学 (Radiomics) 是一种无线电学.

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