aceAn Automated Machine Learning Based Framework for Scan Level Coronary Artery Calcium Risk Stratification
Coronary artery calcium (CAC) scoring plays a crucial role in the early detection and risk stratification of coronary artery disease (CAD). Current techniques for CAC analysis rely on manual annotations by clinicians and large curated training datasets. This study introduces a fully automatic framework for CAC risk stratification that eliminates the need for manual clinical annotations. We introduce a novel pixel-attenuation-based feature extraction pipeline for automatic CAC assessment that leverages pseudo-lab…
A pixel-attenuation machine learning pipeline can classify coronary artery calcium risk groups from non-contrast CT scans without manual clinical annotations, which could ease early stratification of coronary artery disease.
The main training and testing dataset was a single in-house cohort of 182 patients, so the results may not carry over to other populations or settings without wider validation.
Evidence
- Peer-reviewedJournal of Imaging Informatics in Medicine2026-10-05
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Truvace Impact Record TRV-2026-1847, v1: “An Automated Machine Learning Based Framework for Scan Level Coronary Artery Calcium Risk Stratification.” Truvace, 2026-10-18. /record/TRV-2026-1847 (accessed at citation time). sha256 5fc7f63f9bebcd8d…
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