AI-Powered Detection of Left Ventricular Myocardial Scar from Dual-Sequence CT: Global and Segmental Prediction Validated by CMR​.

Abstract

Myocardial scar is a critical pathological substrate, often identified using cardiac magnetic resonance (CMR) as the gold standard. However, CMR accessibility is limited by cost, time, and expertise requirements. This study aimed to develop an artificial intelligence (AI)-powered dual-sequence computed tomography (CT) framework integrating coronary CT angiography (CCTA) and coronary artery calcium (CAC) for detecting myocardial scar, using CMR as the reference. We retrospectively included patients who underwent CCTA, CAC, and CMR. A deep learning nnU-Net model enabled automated cardiac segmentation and rule-based subdivision following the American Heart Association 17-segment model. Radiomic features were extracted from CCTA and CAC, along with volumetric indices, stenosis grading, and clinical/echocardiographic variables. A total of 533 patients were included. CCTA radiomics achieved an AUC of 0.85 at the patient level. Dual-sequence analyses were performed in 257 patients. In this subcohort, the integrated CT model achieved 0.87 and the multimodal model combining CT, clinical, and echocardiographic features achieved an AUC of 0.91. At the segmental level, the combined CCTA-CAC model yielded an AUC of 0.80 among patients with CMR-confirmed myocardial scar. AI-derived dual-sequence CT features demonstrated good discriminatory performance for detecting myocardial scar at the patient level and identifying scar-positive regions at the segmental level. By integrating CT features with clinical and echocardiographic variables, the multimodal model achieved favorable patient-level performance. These findings support the potential role of the proposed CT-based framework in providing complementary scar-related information and identifying high-risk patients who may benefit from further CMR evaluation in appropriate clinical settings.