以人工智能为基础的冠状动脉计算机断层扫描血管学对动脉样硬化负担的量化:与INVICTUS注册表中的血管内超声波进行比较
Rine Nakanishi1, Ryo Okubo2, Hitoshi Matsuo3
1Department of Cardiovascular Medicine, Toho University Graduate School of Medicine, Toho University Omori Medical Center, Tokyo, Japan.
基于人工智能的冠状动脉计算机断层扫描血管造影量化 (AI-QCT) 准确评估动脉样硬化负担. AI-QCT显示了与血管内超声波 (IVUS) 对斑块体积和的高相关性,有助于评估心血管风险.
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
- 动脉样硬化量化的量化
背景情况:
- 动脉样硬化负担是心血管事件的关键预测因素.
- 准确量化冠状动脉样硬化对于风险分层和管理至关重要.
- 自动化人工智能 (AI) 提供了标准化解释和减少冠状动脉计算机断层扫描血管学 (CCTA) 变异性的潜力.
研究的目的:
- 调查基于AI的CCTA量化 (AI-QCT) 对冠状动脉样硬化的诊断实用性.
- 为了比较AI-QCT测量与血管内超声波 (IVUS) 在患病和非患病的冠状动脉部分.
主要方法:
- 来自INVICTUS注册表 (NCT04066062) 的患者接受了CCTA和IVUS治疗.他们被录取.
- 独立的核心实验室量化了外部弹性膜 (EEM),光线,斑块体积,斑块负担和百分比动脉瘤体积 (PAV).
- 分析包括带有非化和低衰减斑块的整个部分和子部分;计算了指数.
主要成果:
- 在整个细分分析中,在AI-QCT和IVUS之间观察到EEM体积,光量,斑块体积,长度正常化的PAV和指数的强烈相关性 (皮尔森r ≥0.833).
- 对于非化 (皮尔森r ≥0.83) 和低衰减斑块段 (皮尔森r ≥0.86) 保持高相关性.
- AI-QCT在光膜区域和光膜区域狭窄方面与IVUS有着密切的一致性.
结论:
- AI-QCT提供了精确可靠的动脉样硬化负担量化.
- 该方法在各种斑块类型和疾病严重程度上与IVUS有很高的相关性和一致性.
- AI-QCT 能够为临床决策提供快速,自动化和标准化的动脉样硬化评估.
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