A machine learning model using Agatston score, eight calcium-omics features, and age from non-contrast CTCS predicts myocardial ischemia with 98.9% precision and 79.2% sensitivity in a single-center cohort of 987 patients.
ArXiv arXiv:2401.16190v1
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CatBoost model with 14 SHAP-selected calcium-omics and fat-omics features from CTCS predicts obstructive CAD at 85.3% accuracy in 1,324 SCOT-HEART patients.
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Quantitative coronary calcification analysis for prediction of myocardial ischemia using non-contrast CT calcium scoring
A machine learning model using Agatston score, eight calcium-omics features, and age from non-contrast CTCS predicts myocardial ischemia with 98.9% precision and 79.2% sensitivity in a single-center cohort of 987 patients.
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Machine learning prediction of obstructive coronary artery disease using opportunistic coronary calcium and epicardial fat assessments from CT calcium scoring scans
CatBoost model with 14 SHAP-selected calcium-omics and fat-omics features from CTCS predicts obstructive CAD at 85.3% accuracy in 1,324 SCOT-HEART patients.