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.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.