Haudenchild Christian, Parsa Shyon, Rodriguez Fatima
Department of Medicine, Stanford University Hospital, Stanford, CA, USA.
Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA.
Curr Atheroscler Rep. 2025 Jul 12;27(1):71. doi: 10.1007/s11883-025-01318-7.
This review summarizes the role of incidentally and non-incidentally discovered coronary artery calcification (CAC) and the evolving role of non-coronary artery calcification in atherosclerotic cardiovascular disease (ASCVD) risk assessment. Additionally, this review explores the emerging use of artificial intelligence (AI), machine learning (ML), radiomics, and natural language processing (NLP) for automated detection, quantification, and communication of these incidentally discovered findings.
This review summarizes recent findings in the space, including the development of various AI/ML-based approaches for automated calcification quantification and detection. Recent work leverages the use of incidentally discovered CAC and non-coronary calcification (e.g. aortic valve, aortic arch, carotid artery, breast arterial calcification) and their influence on clinical decision-making and prescribing practices. CAC and various forms of non-coronary artery calcifications are increasingly recognized as powerful and additive predictors of ASCVD risk. Advances in AI, ML, and radiomics enable scalable, automated measurement of both incidental and non-incidental CAC and non-coronary calcifications, which will facilitate more precise, personalized ASCVD risk stratification.
本综述总结了偶然发现和非偶然发现的冠状动脉钙化（CAC）的作用，以及非冠状动脉钙化在动脉粥样硬化性心血管疾病（ASCVD）风险评估中不断演变的作用。此外，本综述探讨了人工智能（AI）、机器学习（ML）、放射组学和自然语言处理（NLP）在自动检测、量化和传达这些偶然发现结果方面的新兴应用。
本综述总结了该领域的最新发现，包括开发各种基于AI/ML的自动钙化量化和检测方法。最近的研究利用了偶然发现的CAC和非冠状动脉钙化（如主动脉瓣、主动脉弓、颈动脉、乳腺动脉钙化）及其对临床决策和处方实践的影响。CAC和各种形式的非冠状动脉钙化越来越被认为是ASCVD风险的有力且补充性的预测指标。AI、ML和放射组学的进展使得能够对偶然和非偶然的CAC及非冠状动脉钙化进行可扩展的自动测量，这将有助于更精确、个性化的ASCVD风险分层。
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