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人工智能辅助冠状动脉计算机断层扫描血管造影在动脉粥样硬化斑块特征分析方面的进展。

Advances in Artificial Intelligence-Assisted Coronary Computed Tomographic Angiography for Atherosclerotic Plaque Characterization.

作者信息

Chen Qian, Zhou Fan, Xie Guanghui, Tang Chun Xiang, Gao Xiaofei, Zhang Yamei, Yin Xindao, Xu Hui, Zhang Long Jiang

机构信息

Department of Radiology, Nanjing First Hospital, Nanjing Medical University, 210006 Nanjing, Jiangsu, China.

Department of Radiology, Jinling Hospital, Medical School of Nanjing University, 210002 Nanjing, Jiangsu, China.

出版信息

Rev Cardiovasc Med. 2024 Jan 15;25(1):27. doi: 10.31083/j.rcm2501027. eCollection 2024 Jan.

Abstract

Coronary artery disease is a leading cause of death worldwide. Major adverse cardiac events are associated not only with coronary luminal stenosis but also with atherosclerotic plaque components. Coronary computed tomography angiography (CCTA) enables non-invasive evaluation of atherosclerotic plaque along the entire coronary tree. However, precise and efficient assessment of plaque features on CCTA is still a challenge for physicians in daily practice. Artificial intelligence (AI) refers to algorithms that can simulate intelligent human behavior to improve clinical work efficiency. Recently, cardiovascular imaging has seen remarkable advancements with the use of AI. AI-assisted CCTA has the potential to facilitate the clinical workflow, offer objective and repeatable quantitative results, accelerate the interpretation of reports, and guide subsequent treatment. Several AI algorithms have been developed to provide a comprehensive assessment of atherosclerotic plaques. This review serves to highlight the cutting-edge applications of AI-assisted CCTA in atherosclerosis plaque characterization, including detecting obstructive plaques, assessing plaque volumes and vulnerability, monitoring plaque progression, and providing risk assessment. Finally, this paper discusses the current problems and future directions for implementing AI in real-world clinical settings.

摘要

冠状动脉疾病是全球主要的死亡原因之一。主要不良心脏事件不仅与冠状动脉管腔狭窄有关,还与动脉粥样硬化斑块成分有关。冠状动脉计算机断层扫描血管造影(CCTA)能够对整个冠状动脉树的动脉粥样硬化斑块进行无创评估。然而,在日常实践中,对CCTA上的斑块特征进行精确有效的评估对医生来说仍然是一项挑战。人工智能(AI)是指能够模拟人类智能行为以提高临床工作效率的算法。近年来,心血管成像领域在人工智能的应用方面取得了显著进展。AI辅助CCTA有潜力促进临床工作流程,提供客观且可重复的定量结果,加快报告解读,并指导后续治疗。已经开发了几种AI算法来对动脉粥样硬化斑块进行全面评估。本综述旨在突出AI辅助CCTA在动脉粥样硬化斑块特征描述方面的前沿应用,包括检测阻塞性斑块、评估斑块体积和易损性、监测斑块进展以及提供风险评估。最后,本文讨论了在实际临床环境中实施AI的当前问题和未来方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2db9/11262402/728598a3911a/2153-8174-25-1-027-g1.jpg

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