Prognostic value of end-to-end deep learning assessment of myocardial scar and microvascular obstruction on late gadolinium enhancement cardiovascular magnetic resonance.

BACKGROUND

Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) is the reference standard for assessing myocardial scar and microvascular obstruction (MVO), and is a strong predictor of post-acute myocardial infarction (AMI) outcomes. However, manual segmentation is time-consuming and subject to inter-observer variability, limiting clinical scalability. This study develops and validates LGE-CMRnet, an end-to-end deep learning pipeline for automated scar and MVO segmentation on LGE CMR, and evaluates its prognostic value in AMI patients.

METHODS

A total of 3874 LGE images from 567 AMI patients (409 for training/internal stress-test cohort; 158 for external testing) were analyzed. LGE-CMRnet integrates YOLOv8 for heart localization and nnU-Net for simultaneous segmentation of myocardium, scar, and MVO. Performance was evaluated using Dice similarity coefficient (DSC), correlation, and Bland-Altman analysis against expert annotations. Prognostic value was assessed using Cox regression for major adverse cardiac events (MACE) over a median follow-up of 24.4 months.

RESULTS

LGE-CMRnet achieved rapid processing (0.05 s per image) and high segmentation accuracy. In the external validation cohort, the model achieved mean DSC of 0.83 ± 0.11 for scar and 0.88 ± 0.11 for MVO at the patient level, with strong volumetric correlations to expert reference segmentations (scar: r = 0.90; MVO: r = 0.98, both P<0.0001). Bland-Altman analysis showed minimal bias in volumetric measurements (scar: 2.5 ± 8.9 cm3; MVO: -0.20 ± 0.89 cm3). Among the 158 patients in the external validation cohort (age 57 ± 10 years, 127 male), 35 (22.2%) experienced MACE. LGE-CMRnet-derived %MVO (hazard ratio [HR], 1.06; 95% confidence interval [CI]: 1.02 to 1.09; P = 0.003) and %Scar (HR, 1.05; 95% CI: 1.02 to 1.08; P = 0.001) were independent predictors of MACE after adjustment for established risk factors. Furthermore, LGE-CMRnet-derived metrics demonstrated non-inferior discrimination for MACE prediction compared with expert analysis. The differences in C-index were 0.02 for %MVO and 0.01 for %Scar, with the lower bounds of the 95% CIs remaining above the pre-specified non-inferiority margin.

CONCLUSION

LGE-CMRnet enables fast and accurate scar and MVO quantification, with prognostic performance comparable to expert analysis, supporting its potential for automated clinical risk stratification after AMI.