A novel deep learning system for STEMI prognostic prediction from multi-sequence cardiac magnetic resonance

作者：Yi‐Fan Chen, Meng Jiang, Chao Xia, Hang Zhao, Ke Peng, Sheng Chen, Heng Ge, Keran Li, Xu Wang, Yufei Wang, Yezi Chai, Qiming Liu, Zhengyu Tao, Yuyan Lyu, Yina Wu, Ao Shi, Yang Liu, Hongyi Xin, Yu Lin Zhong, Wei Zhang, Fuhua Yan, Weiwei Quan, Ying‐Jia Xu, Dan Liu, Yu‐Min Sun, Xinli Li, Yuanyuan Tian, Lian‐Ming Wu, Shengxian Tu, Hongwei Ji, Bin Sheng, Jun Pu · 发表于：中国科学通报：英文版 · 年份：2025 · DOI：10.1016/j.scib.2025.11.027 · 被引用次数：35 · 研究领域：Cardiac Imaging and Diagnostics、Acute Myocardial Infarction Research、Cardiovascular Function and Risk Factors

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 p...