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人工智能在冠状动脉CT血管造影中的作用。

The role of artificial intelligence in coronary CT angiography.

作者信息

van Herten Rudolf L M, Lagogiannis Ioannis, Leiner Tim, Išgum Ivana

机构信息

Department of Biomedical Engineering and Physics, Amsterdam University Medical Center-location University of Amsterdam, Amsterdam, The Netherlands.

Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands.

出版信息

Neth Heart J. 2024 Nov;32(11):417-425. doi: 10.1007/s12471-024-01901-8. Epub 2024 Oct 10.

DOI:10.1007/s12471-024-01901-8
PMID:39388068
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11502768/
Abstract

Coronary CT angiography (CCTA) offers an efficient and reliable tool for the non-invasive assessment of suspected coronary artery disease through the analysis of coronary artery plaque and stenosis. However, the detailed manual analysis of CCTA is a burdensome task requiring highly skilled experts. Recent advances in artificial intelligence (AI) have made significant progress toward a more comprehensive automated analysis of CCTA images, offering potential improvements in terms of speed, performance and scalability. This work offers an overview of the recent developments of AI in CCTA. We cover methodological advances for coronary artery tree and whole heart analysis, and provide an overview of AI techniques that have shown to be valuable for the analysis of cardiac anatomy and pathology in CCTA. Finally, we provide a general discussion regarding current challenges and limitations, and discuss prospects for future research.

摘要

冠状动脉CT血管造影（CCTA）通过分析冠状动脉斑块和狭窄情况，为疑似冠状动脉疾病的无创评估提供了一种高效且可靠的工具。然而，对CCTA进行详细的人工分析是一项繁重的任务，需要高技能的专家。人工智能（AI）的最新进展在对CCTA图像进行更全面的自动分析方面取得了重大进展，在速度、性能和可扩展性方面提供了潜在的改进。这项工作概述了AI在CCTA方面的最新进展。我们涵盖了冠状动脉树和全心分析的方法学进展，并概述了已证明对CCTA中心脏解剖结构和病理学分析有价值的AI技术。最后，我们对当前的挑战和局限性进行了一般性讨论，并探讨了未来研究的前景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7906/11502768/195e9db0da42/12471_2024_1901_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7906/11502768/c270901d5927/12471_2024_1901_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7906/11502768/1d5bf3ea16e0/12471_2024_1901_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7906/11502768/1fa4f978df3d/12471_2024_1901_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7906/11502768/195e9db0da42/12471_2024_1901_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7906/11502768/c270901d5927/12471_2024_1901_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7906/11502768/1d5bf3ea16e0/12471_2024_1901_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7906/11502768/1fa4f978df3d/12471_2024_1901_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7906/11502768/195e9db0da42/12471_2024_1901_Fig4_HTML.jpg

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本文引用的文献

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2
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Nat Rev Cardiol. 2024 Jan;21(1):51-64. doi: 10.1038/s41569-023-00900-3. Epub 2023 Jul 18.
3
Clinical quantitative coronary artery stenosis and coronary atherosclerosis imaging: a Consensus Statement from the Quantitative Cardiovascular Imaging Study Group.
冠状动脉CT血管造影中的人工智能：改变动脉粥样硬化的诊断和风险分层
Int J Cardiovasc Imaging. 2025 Jun 27. doi: 10.1007/s10554-025-03440-8.
4
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Nat Rev Cardiol. 2023 Oct;20(10):696-714. doi: 10.1038/s41569-023-00880-4. Epub 2023 Jun 5.
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