Interpretable and reproducible machine learning model for coronary calcification and segment-level stenoses stratification on computed tomography angiography
Jian Chen; Hongqiu Wang; Yiran Wei; Yancheng Xu; Guangming Wang; Y F Li; Zeyu Gao; Kaixuan Li · 2025 · BMC Medicine
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
BACKGROUND: Coronary computed tomography angiography (CCTA) is widely used as a first-line tool for diagnosing and managing coronary artery disease (CAD), and machine learning (ML)-based analysis shows promise for quantitative CAD assessment. METHODS: In this post hoc analysis of 909 participants from the SCOT-HEART trial (median follow-up, 5.8 years), we first evaluated the distribution of CCTA-derived imaging features in a cohort (n = 221) with a zero calcium score, stenoses < 10%, and no evidence of CAD on CCTA, across 21 image processing settings. Interpretable ML models were then develope
Abstract by Jian Chen; Hongqiu Wang; Yiran Wei; Yancheng Xu; Guangming Wang; Y F Li; Zeyu Gao; Kaixuan Li, BMC Medicine (2025) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1186/s12916-025-04478-0