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应用于冠状动脉钙化扫描的人工智能（AI-CAC）显著改善心血管事件预测。

Artificial intelligence applied to coronary artery calcium scans (AI-CAC) significantly improves cardiovascular events prediction.

作者信息

Naghavi Morteza, Reeves Anthony P, Atlas Kyle, Zhang Chenyu, Atlas Thomas, Henschke Claudia I, Yankelevitz David F, Budoff Matthew J, Li Dong, Roy Sion K, Nasir Khurram, Molloi Sabee, Fayad Zahi, McConnell Michael V, Kakadiaris Ioannis, Maron David J, Narula Jagat, Williams Kim, Shah Prediman K, Levy Daniel, Wong Nathan D

机构信息

HeartLung.AI, Houston, TX, 77021, USA.

Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, 14853, USA.

出版信息

NPJ Digit Med. 2024 Nov 5;7(1):309. doi: 10.1038/s41746-024-01308-0.

DOI:10.1038/s41746-024-01308-0
PMID:39501071
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11538462/
Abstract

Coronary artery calcium (CAC) scans contain valuable information beyond the Agatston Score which is currently reported for predicting coronary heart disease (CHD) only. We examined whether new artificial intelligence (AI) applied to CAC scans can predict non-CHD events, including heart failure, atrial fibrillation, and stroke. We applied AI-enabled automated cardiac chambers volumetry and calcified plaque characterization to CAC scans (AI-CAC) of 5830 asymptomatic individuals (52.2% women, age 61.7 ± 10.2 years) in the multi-ethnic study of atherosclerosis during 15 years of follow-up, 1773 CVD events accrued. The AUC at 1-, 5-, 10-, and 15-year follow-up for AI-CAC vs. Agatston score was (0.784 vs. 0.701), (0.771 vs. 0.709), (0.789 vs. 0.712) and (0.816 vs. 0.729) (p < 0.0001 for all), respectively. AI-CAC plaque characteristics, including number, location, density, plus number of vessels, significantly improved CHD prediction in the CAC 1-100 cohort vs. Agatston Score. AI-CAC significantly improved the Agatston score for predicting all CVD events.

摘要

冠状动脉钙化（CAC）扫描包含的有价值信息超出了目前仅用于预测冠心病（CHD）的阿加斯顿评分。我们研究了应用于CAC扫描的新型人工智能（AI）是否能够预测非冠心病事件，包括心力衰竭、心房颤动和中风。在动脉粥样硬化多民族研究中，我们对5830名无症状个体（52.2%为女性，年龄61.7±10.2岁）的CAC扫描（AI-CAC）应用了人工智能驱动的自动心腔容积测量和钙化斑块特征分析，在15年的随访期间，共发生了1773例心血管疾病（CVD）事件。AI-CAC与阿加斯顿评分在1年、5年、10年和15年随访时的曲线下面积（AUC）分别为（0.784对0.701）、（0.771对0.709）、（0.789对0.712）和（0.816对0.729）（所有p均<0.0001）。与阿加斯顿评分相比，AI-CAC斑块特征，包括数量、位置、密度以及血管数量，在CAC 1-100队列中显著改善了冠心病预测。AI-CAC在预测所有CVD事件方面显著优于阿加斯顿评分。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/557e/11538462/3a08ba41aa5e/41746_2024_1308_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/557e/11538462/bd16436031df/41746_2024_1308_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/557e/11538462/74c92c70cb5f/41746_2024_1308_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/557e/11538462/3a08ba41aa5e/41746_2024_1308_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/557e/11538462/bd16436031df/41746_2024_1308_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/557e/11538462/74c92c70cb5f/41746_2024_1308_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/557e/11538462/3a08ba41aa5e/41746_2024_1308_Fig3_HTML.jpg

相似文献

[1]
Artificial intelligence applied to coronary artery calcium scans (AI-CAC) significantly improves cardiovascular events prediction.

NPJ Digit Med. 2024-11-5

[2]
AI-enabled Cardiac Chambers Volumetry and Calcified Plaque Characterization in Coronary Artery Calcium (CAC) Scans (AI-CAC) Significantly Improves on Agatston CAC Score for Predicting All Cardiovascular Events: The Multi-Ethnic Study of Atherosclerosis.

Res Sq. 2024-6-20

[3]
AI-enabled cardiac chambers volumetry in coronary artery calcium scans (AI-CAC) predicts heart failure and outperforms NT-proBNP: The multi-ethnic study of Atherosclerosis.

J Cardiovasc Comput Tomogr. 2024

[4]
AI-enabled left atrial volumetry in coronary artery calcium scans (AI-CAC) predicts atrial fibrillation as early as one year, improves CHARGE-AF, and outperforms NT-proBNP: The multi-ethnic study of atherosclerosis.

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[5]
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[6]
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[8]
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[10]
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引用本文的文献

[1]
Inadequacy of coronary calcium scoring in evaluating coronary artery disease: A call to shifting to high-resolution CT coronary imaging.

Int J Cardiol Cardiovasc Risk Prev. 2025-7-18

[2]
Application of non-invasive imaging in myocardial infarction: a bibliometric analysis from January 2003 to December 2022.

Quant Imaging Med Surg. 2025-7-1

[3]
Artificial Intelligence in Ischemic Heart Disease Prevention.

Curr Cardiol Rep. 2025-2-1

[4]
Fully Automated Assessment of Cardiac Chamber Volumes and Myocardial Mass on Non-Contrast Chest CT with a Deep Learning Model: Validation Against Cardiac MR.

Diagnostics (Basel). 2024-12-21

[5]
Artificial Intelligence-Driven Advances in Coronary Calcium Scoring: Expanding Preventive Cardiology.

Cureus. 2024-11-28

本文引用的文献

[1]
AI-enabled cardiac chambers volumetry in coronary artery calcium scans (AI-CAC) predicts heart failure and outperforms NT-proBNP: The multi-ethnic study of Atherosclerosis.

J Cardiovasc Comput Tomogr. 2024

[2]
AI-enabled left atrial volumetry in coronary artery calcium scans (AI-CAC) predicts atrial fibrillation as early as one year, improves CHARGE-AF, and outperforms NT-proBNP: The multi-ethnic study of atherosclerosis.

J Cardiovasc Comput Tomogr. 2024

[3]
AI-Defined Cardiac Anatomy Improves Risk Stratification of Hybrid Perfusion Imaging.

JACC Cardiovasc Imaging. 2024-7

[4]
TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images.

Radiol Artif Intell. 2023-7-5

[5]
Patient Exposure from Radiologic and Nuclear Medicine Procedures in the United States and Worldwide: 2009-2018.

Radiology. 2023-4

[6]
Prevalence and significance of risk enhancing biomarkers in the United States population at intermediate risk for atherosclerotic disease.

J Clin Lipidol. 2022

[7]
Socioeconomics of coronary artery calcium: Is it scored or ignored?

J Cardiovasc Comput Tomogr. 2022

[8]
Role of Coronary Artery Calcium Testing for Risk Assessment in Primary Prevention of Atherosclerotic Cardiovascular Disease: A Review.

JAMA Cardiol. 2022-2-1

[9]
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.

Nat Methods. 2021-2

[10]
Machine Learning Outperforms ACC / AHA CVD Risk Calculator in MESA.

J Am Heart Assoc. 2018-11-20

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