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Abstract #3814

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

Xuefang Lu1, Yuchen Yan1, Weiyin Vivian Liu2, and Yunfei Zha1
1Department of radiology, Renmin Hospital Wuhan University, Wuhan, China, 2GE Healthcare, MR Research China, Beijing, China

Synopsis

Keywords: AI/ML Image Reconstruction, Cardiovascular

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.

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Keywords