A teacher–student deep learning framework for enhanced clinical screening of heart failure from 12-lead electrocardiograms

Objective. Identifying heart failure (HF) from electrocardiograms (ECGs) is challenging due to the lack of definitive features. This study aims to develop a deep learning-based clinical decision support system, CTTSnet, for accurate and automated HF screening using ECGs. Approach. We propose a novel teacher–student framework, CTTSnet, which synergistically integrates a transformer as the teacher and a convolutional neural network as the student. This architecture combines global context-awareness with efficient local feature extraction. The model was trained and evaluated on a large-scale, real-world dataset of 27 018 ECGs and further validated on two external public datasets to assess generalizability. Main results. CTTSnet achieved an AUROC of 0.941 on the primary clinical dataset, surpassing strong baselines. The model attained an accuracy of 87.3%, with a recall of 88.6% and specificity of 85.8%. External validation on two additional public datasets further confirmed the model’s generalizability. Significance. CTTSnet provides a scalable, high-accuracy tool for HF triage from routine ECG recordings. Its potential clinical value lies in assisting early identification of patients at risk of HF, thereby helping reduce missed diagnoses and facilitating timely referral for further evaluation.