Abstract
BACKGROUND The diagnosis of myocarditis is challenging, and the treatment is generally delayed due to misdiagnosis or missed diagnosis. Endomyocardial biopsy (EMB) is not a specific or sensitive method. A case-controlled observational study was conducted to evaluate early gadolinium enhancement (EGE) and left ventricular functional parameters on Artificial Intelligence in cine-MRI in patients with acute myocarditis. MATERIAL AND METHODS We selected 21 patients with pathologically proven acute myocarditis. We analyzed the EGE findings (total/serial number and location of positive-segments using the 17-segment model according to the American Heart Association) and clinical characteristics (symptoms, arrhythmias in ECG, coronary angiography, and EMB). All patients were divided into positive EGE and negative EGE groups to analyze left ventricular functional parameters (LVEF, FS, LVEDD, LVEDV, LVESV, LVMM, LVSV, CO, and CI) on Artificial Intelligence. RESULTS We enrolled 21 patients (11 males) with a mean age of 32.6±9.8 years (range, 16 to 51 years). Abnormalities on EGE were found in 2/3 of patients, involving 41 segments among multiple locations on the myocardium. The differences in LVEF (40.2±10.2% vs. 51.3±3.6%), LVESV (69.0±16.1ml vs. 52.5±10.6ml) and LVSV (42.6±11.4 vs. 52.8±2.8 ml) on Artificial Intelligence was statistically significant between the positive EGE and negative EGE groups (p<0.05). CONCLUSIONS Our results suggest a significant role of EGE on the basis of Lake Louise criteria in evaluating patients with clinical suspicion of acute myocarditis. Parameters, including LVEF, LVESV, and LVSV, on Artificial Intelligence, may be useful independent predictors for capillary leakage and microcirculatory disturbance in myocarditis.