Association of Deep Learning-based Myocardial Infarction Size Quantification in Cardiac MRI with Cardiac Biomarker...

Purpose To evaluate the reliability and clinical applicability of an artificial intelligence (AI)-based infarct size quantification method based on cardiac MR images in patients with ST-elevation myocardial infarction (STEMI). Materials and Methods This retrospective study included patients with acute STEMI who underwent cardiac MRI between January 2005 and October 2024. A convolutional neural network (CNN) was trained on 468 unique cardiac MRI examinations, with manual infarct segmentations serving as the reference standard. On a test set, correlations between manual and AI-determined infarct sizes and peak creatine kinase (CK) and cardiac troponin T (cTnT) levels were assessed using Pearson and Spearman correlation analyses. The predictive value of the manual and AI-based measurements for the occurrence of left ventricular adverse remodeling (LVAR) was compared using the DeLong test. Results The test set comprised 800 patients (median age, 58 years [IQR, 51-67 years]; 83% male). The CNN estimated a larger median infarct size than the manual measurements did (26.5 vs 20.1 mL; P < .001). The correlation with peak CK levels was greater (P < .001) for the automated measurements (r = 0.76, ρ = 0.80) than for manual segmentations (r = 0.68, ρ = 0.72). The same relationship was observed for peak cTnT levels (r = 0.66 vs r = 0.57; P = .004). Manual and AI-based measurements demonstrated comparable predictive value for LVAR (P = .24). Conclusion The AI-based infarct size quantification method based on cardiac MRI is comparable to manual measurement and is strongly correlated with cardiac biomarkers. Keywords: MR-Imaging, Cardiac, Heart, Ischemia/Infarction, Segmentation, Late Gadolinium Enhancement, ST Elevation Myocardial Infarction, Convolutional Neural Networks, Cardiac Biomarkers Supplemental material is available for this article. © The Author(s) 2026. Published by the Radiological Society of North America under a CC BY 4.0 license. ClinicalTrials.gov identifier: NCT04113356.