Interpretable and reproducible machine learning model for coronary calcification and segment-level stenoses stratification on computed tomography angiography

Stable imaging features provide a reference for future ML-based coronary quantitatively assessments. Interpretable ML models demonstrated promising performance in quantifying coronary calcification and segment-level stenoses.

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Publication Details

Journal
BMC Medicine
Published
2025-11-26
DOI
https://doi.org/10.1186/s12916-025-04478-0
Citations
1
Primary Topic
Cardiac Imaging and Diagnostics
Type
article
Field-Weighted Citation Impact
1.36

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article

Interpretable and reproducible machine learning model for coronary calcification and segment-level stenoses stratification on computed tomography angiography

Xiaowei Zhou, Zheng Jin, Y F Li, L. Escudero et al.
1 citations
BMC Medicine
Cardiac Imaging and Diagnostics
1.36
article

Interpretable and reproducible machine learning model for coronary calcification and segment-level stenoses stratification on computed tomography angiography

Xiaowei Zhou, Zheng Jin, Y F Li, L. Escudero, Yancheng Xu, Jonathan Richard Weir-McCall, Yuan Huang, Zeyu Gao, James H.F. Rudd, David E. Newby, Hongqiu Wang, Guangming Wang, Zhongzhao Teng, Ziming Wang, Jian Chen, Kaixuan Li, Yiran Wei, Michelle C. Williams
article en
1 citations

Abstract

Stable imaging features provide a reference for future ML-based coronary quantitatively assessments. Interpretable ML models demonstrated promising performance in quantifying coronary calcification and segment-level stenoses.

BMC MedicineVol. 23(1)
Central South University (CN), King's College London (GB), Hong Kong University of Science and Technology (HK), University of Cambridge (GB), Royal Papworth Hospital (GB), British Heart Foundation (GB), Cambridge School (PT), Xiangya Hospital Central South University (CN), Fujian University of Technology (CN), University of Edinburgh (GB)
Scottish Government, National Institute for Health and Care Research, British Heart Foundation, Cambridge Trust, Chief Scientist Office, Scottish Government Health and Social Care Directorate, NIHR Cambridge Biomedical Research Centre
Openalex Percentile: Top 14%
Cardiac Imaging and Diagnostics
1.36
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