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Multi-Scale Attention Network for Myocardial Infarction Transmurality Classification in Late Gadolinium Enhancement
Insights
A new AI model, MSAN-TC, accurately classifies myocardial infarction extent using cardiac MRI. This automated approach improves upon manual methods for assessing heart attack severity and prognosis.
Area of Science:
- Cardiovascular Imaging
- Artificial Intelligence in Medicine
- Medical Image Analysis
Background:
- Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) imaging is crucial for assessing myocardial infarction (MI) severity and prognosis.
- Current visual assessment of LGE images has limitations, including inter-observer variability and reliance on manual segmentation.
Purpose of the Study:
- To develop and evaluate an automated method for classifying the transmural extent of myocardial infarction using LGE CMR images.
- To address the limitations of manual assessment by introducing a novel deep learning model.
Main Methods:
- A Multi-Scale Attention Network for Transmurality Classification (MSAN-TC) was proposed, integrating CNNs, Transformer models, feature pyramid networks (FPN), and channel attention (CA).
- The model was trained and evaluated on 1,821 LGE CMR images from 315 patients, utilizing weakly labeled data for classification.
Main Results:
- MSAN-TC achieved an overall accuracy of 86% in transmurality classification.
- The model demonstrated a high area under the curve (AUC) of 0.90 for detecting ≥50% transmural infarction.
- High sensitivity (91%) and specificity (89%) were observed in the classification task.
Conclusions:
- The MSAN-TC model offers an automated, efficient, and clinically practical solution for assessing myocardial infarction extent.
- This deep learning approach represents a significant step towards scalable and reliable MI assessment in real-world clinical settings.
Abstract:
The transmural extent of hyperenhancement on late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) imaging is a key marker of myocardial infarction severity and prognosis. Current visual assessment methods suffer from inter-observer variability and reliance on manual segmentation. In this paper, we propose a Multi-Scale Attention Network for Transmurality Classification (MSAN-TC) using LGE CMR images. MSAN-TC integrates convolutional neural networks (CNNs) and Transformer models with feature pyramid networks (FPN) and channel attention (CA) mechanisms, enabling accurate classification of infarction extent from weakly labeled data. Evaluated on 1,821 images from 315 patients, MSAN-TC achieves an overall accuracy of 86% in transmurality classification and an area under the curve (AUC) of 0.90 in detecting ≥50% transmural infarction, demonstrating high sensitivity (91%) and specificity (89%). This work represents a step towards automated, efficient, and clinically practical myocardial infarction assessment, providing a scalable solution for real-world applications.
