Machine Learning Model for Atherosclerosis Evaluation and Cardiovascular Risk Prediction Based on Coronary CT Angiography-Analysis From the CREATION Registry
The machine learning model incorporating CCTA plaque quantification, characterization, and stenosis assessment significantly enhanced the predictive capacity for major adverse cardiac events. It provides direct visualization of coronary atherosclerosis and outperforms the traditional risk factors and the coronary artery calcium score model recommended in clinical practice.
Authors
- Jinqing Yuan (ORCID: https://orcid.org/0000-0002-3740-5103)
- Sida Jia
- Ying Song
- Bin Lü (ORCID: https://orcid.org/0000-0002-1744-7584)
- Cheng Cui (ORCID: https://orcid.org/0000-0001-8402-6459)
- Jianan Zheng (ORCID: https://orcid.org/0000-0002-7518-9675)
- Lijian Gao
- Yin Zhang (ORCID: https://orcid.org/0000-0002-3101-3627)
- Jue Chen (ORCID: https://orcid.org/0000-0003-2075-4283)
- Zhan Gao (ORCID: https://orcid.org/0000-0003-3957-8718)
- Na Xu (ORCID: https://orcid.org/0000-0002-7081-3360)
- Lei Song (ORCID: https://orcid.org/0000-0002-6657-9516)
- Zhihui Hou (ORCID: https://orcid.org/0009-0008-8357-8217)
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Fu Wai Hospital (CN)
Publication Details
- Journal
- Circulation Cardiovascular Imaging
- Published
- 2025-11-21
- DOI
- https://doi.org/10.1161/circimaging.125.018443
- Primary Topic
- Cardiac Imaging and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 0.00