Combing CT coronary artery calcification score and deep learning MR late gadolinium enhancement to detect unrecognized myocardial infarction

Motivation: Coronary artery calcification score (CACS) is currently a common and widely-accepted indication of UMI, but it itself fails to accurately reflect myocardial ischemia in patients with unrecognized myocardial infarction(UMI). Goal(s): To establish a UMI-screening workflow for a cohort who receive a physical examination. Approach: To explore the detection rate of myocardial infarction (MI) using CACS only, Parea only, CACS in combination with Parea using different thresholds. Results: The AI-CACS combined with Parea had higher diagnostic performance on differentiating UMI from non-UMI groups than AI-CACS or Parea alone, especially AI-CACS combined with Parea-DL-5SD with AUC of 0.914. Impact: Patients with UMI usually do not have typical symptoms of cardiogenic chest pain. CACS-Parea-DL-5SD can detect unrecognized myocardial infarction in the outpaitnets, and increased the diagnostic confidence of UMI, providing an important reference for UMI risk stratification and follow-up recommendations.