AI-Based Myocardial Segmentation and Attenuation Mapping Improved Detection of Myocardial Ischemia and Infarction on Emergency CT Angiography

<b>Purpose:</b> To investigate whether an AI-based approach combining deep learning myocardial segmentation with attenuation-normalized myocardial mapping (colormaps) improves detection of myocardial ischemia and infarction on emergency ECG-gated CT angiography. <b>Materials and Methods:</b> In this retrospective study, 119 patients with acute chest pain who underwent ECG-gated CT angiography to exclude pulmonary embolism or acute aortic syndrome and invasive coronary angiography within 48 h were included. A deep learning model (nnU-Net) was used for automatic left-ventricular myocardial segmentation, serving as the basis for voxel-wise attenuation normalization to generate AI-based myocardial attenuation maps. Six readers with varying experience levels evaluated all cases for myocardial hypoattenuation in a multi-reader, multi-case design, with and without AI-generated attenuation maps. <b>Results:</b> AI-based myocardial attenuation mapping increased mean sensitivity for detection of myocardial ischemia or infarction by 12% [IQR 2-20%] compared with standard CT interpretation alone. Sensitivity improved by 15% [IQR 10-22%] in STEMI (ST-Elevation Myocardial Infarction) and 11% [IQR -1-18%] in NSTEMI (Non-STEMI) cases. The AI-assisted approach resulted in the correct reclassification of 11% of patients and improved inter-reader agreement, particularly among less experienced readers, demonstrating reduced reader dependency. <b>Conclusions:</b> AI-based myocardial segmentation and attenuation mapping enhance the detection of myocardial ischemia and infarction on emergency CT angiography and improve inter-reader agreement. This AI-assisted image processing approach provides clinically meaningful decision support in acute chest pain imaging workflows.