A novel deep learning system for STEMI prognostic prediction from multi-sequence cardiac magnetic resonance.
Authors
Affiliations (19)
Affiliations (19)
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China. Electronic address: [email protected].
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China.
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
- Global Institute of Future Technology, Shanghai Jiao Tong University, Shanghai 200240, China.
- Global Institute of Future Technology, Shanghai Jiao Tong University, Shanghai 200240, China; School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai 200240, China.
- Yunnan TBH Biotech & Natural Resources Exploitation Co., Ltd, Kunming 650106, China.
- Department of Radiology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China.
- Department of Vascular & Cardiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
- Department of Cardiology, Shanghai Fifth People's Hospital, Fudan University, Shanghai 201100, China.
- Department of Cardiology, Jing'an District Centre Hospital of Shanghai, Fudan University, Shanghai 200040, China.
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China.
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
- Tsinghua Medicine, Tsinghua University, Beijing 100084, China.
- Department of Computer Science and Engineering, School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University; MOE Key Laboratory of AI, School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. Electronic address: [email protected].
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China. Electronic address: [email protected].
Abstract
ST-elevation myocardial infarction (STEMI) remains a leading cause of cardiovascular morbidity and mortality worldwide, and accurate early risk stratification is critical for implementing precision therapies in clinical practice. However, existing clinical risk scores and manually derived imaging biomarkers have limited accuracy in predicting post-STEMI outcomes. To address this gap, we developed DeepSTEMI, an end-to-end deep learning system that integrates multi-sequence cardiac magnetic resonance (CMR) images with clinical parameters for predicting 2-year major adverse cardiovascular events (MACE). The system comprised two key algorithmic modules: a U-Net module that automatically segments heart regions from raw CMR images and a Transformer-based module that predicted future cardiovascular events. DeepSTEMI was developed using a multicenter dataset (n = 610; 20,618 images) from STEMI patients enrolled in the EARLY-MYO-CMR registry (NCT03768453), with external validation performed in 334 patients (9944 images) from three independent cardiac centers. In external validation, DeepSTEMI demonstrated superior predictive performance compared to conventional clinical risk scores and manual CMR parameters (AUC 0.894, 95% CI: 0.823-0.965; overall accuracy 94.3%). The model identified high-risk patients who exhibited a 20-fold MACE risk compared to low-risk counterparts (HR 20.43, log-rank P < 0.001). SHapley Additive exPlanations (SHAP) analysis revealed that DeepSTEMI's predictive power stems from clinical-imaging synergy, enabling it to capture complex pathological patterns. DeepSTEMI achieved consistently superior performance over the Eitel score across all subgroups, with the greatest benefit observed in women (NRI 1.597) and in patients imaged 4-7 d post-STEMI (NRI 1.442). Overall, DeepSTEMI serves as an automated, scalable, and interpretable clinical copilot, which advances post-STEMI risk stratification beyond the limitations of current paradigms.