Artificial intelligence-based accurate myocardial infarction mapping using 12-lead electrocardiography.

ABSTRACT:

Aims

Assessing myocardial fibrosis (MF) in patients with prior myocardial infarction (MI) is crucial for prognosis. Artificial intelligence-assisted electrocardiography (AI-ECG) has a great potential to detect MF. However, training a precise AI-ECG model requires voluminous ECGs. A biosimulation model may be an efficient substitution. This study aimed to develop and validate a novel artificial intelligence-assisted method using 12-lead electrocardiography (AI-MI-12ECG).

Methods and results

The AI-MI-12ECG was trained by a biosimulation model to visualize the presence, location, and size of MF in post-MI patients. A total of 182 post-MI patients were included in this prospective study. The MF detected by AI-MI-12ECG and the cardiologist were compared with the late gadolinium-

SUBMITTER: Wang H 

PROVIDER: S-EPMC12450511 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature