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开发和验证用于诊断血管特异性冠状动脉缺血症的定量冠状动脉CT血管造影模型

Nick S Nurmohamed¹, Ibrahim Danad², Ruurt A Jukema³

¹Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Division of Cardiology, The George Washington University School of Medicine, Washington, DC, USA.

JACC. Cardiovascular imaging

|March 14, 2024

概括

一个新的人工智能引导的定量冠状动脉计算机断层扫描血管学 (AI-QCT) 模型准确诊断冠状动脉缺血. 这种AI-QCT_ISCHEMIA模型还预测了超出狭窄存在的主要不良心血管事件 (MACE).

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:

冠状动脉缺血的非侵入性压力测试具有可变的准确性和高成本.
对于冠状动脉疾病,需要准确且具有成本效益的诊断工具.

研究的目的:

开发和验证一个AI-QCT模型 (AI-QCT_ISCHEMIA) 整合动脉样硬化和血管形态来诊断冠状动脉缺血.
评估AI-QCT_ISCHEMIA对主要不良心血管事件 (MACE) 的预后价值.

主要方法:

对涉及疑似稳定冠状动脉疾病的症状患者的CREDENCE和PACIFIC-1研究进行了后期分析.
使用冠状动脉计算机断层扫描血管学 (CTA) 数据开发AI-QCT_ISCHEMIA模型.
对侵入性分流储备 (FFR) 的诊断性能和MACE的预后效益的评估.

主要成果:

在验证队列中,AI-QCT_ISCHEMIA显示出与FFR_CT (AUC 0.69-0.78) 和MPI (AUC 0.65-0.72) 相比,更高的诊断准确度 (AUC 0.80-0.85).
在PACIFIC-1中,AI-QCT_ISCHEMIA的准确性与PET的准确性相当 (AUC为0.85对比0.89).
一个阳性AI-QCT_ISCHEMIA测试独立地与MACE风险增加7.6倍相关 (aHR:7.6).

结论:

AI-QCT_ISCHEMIA模型根据冠状动脉CTA准确诊断冠状动脉缺血.
这种人工智能驱动的方法为MACE提供了显著的预后价值,独立于冠状动脉狭窄.
AI-QCT_ISCHEMIA为评估缺血和心血管风险提供了一个有希望的非侵入性工具.

关键词:

人工智能的人工智能是人工智能.动脉样硬化动脉样硬化冠状动脉计算机断层扫描血管图谱冠状动脉缺血症冠状动脉缺血症压力测试是一种压力测试.

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