The deep learning model achieved a diagnostic accuracy of 96.0% for STEMI and 89.1% for NSTEMI, significantly faster than physician interpretation (0.24 ± 0.08s vs. 23.84 ± 9.45s, p < 0.001).
Observational (n=1,188)
Yes
Does an AI-based 12-lead ECG prediction model improve diagnostic accuracy and speed for emergency department patients with acute chest pain compared to expert cardiologists?
An AI-based 12-lead ECG model provides rapid and highly accurate detection of STEMI and NSTEMI in emergency chest pain patients, though it struggles with the non-specific ECG features of unstable angina and aortic dissection.
Effect estimate: null (95% CI null)
Absolute Event Rate: 96% vs 94.5%
p-value: p=<0.001
Background/Objectives Rapid triage and etiological differentiation are critical for patients with acute chest pain in the emergency department. The 12-lead electrocardiogram (ECG), as a non-invasive, readily available, and cost-effective diagnostic modality, provides immediate information and serves as the first-line tool for clinical evaluation. However, ECG interpretation remains highly dependent on clinician expertise and is subject to inter-observer variability. Artificial intelligence (AI)-based analytical methods can deliver automated, consistent, and real-time assessment, thereby potentially enhancing diagnostic accuracy and facilitating timely clinical decision-making. Methods This study included 1,188 patients with acute chest pain who visited the emergency department in the Second Xiangya Hospital of Central South University, between March 2024 and March 2025. Standard 12-lead ECGs, clinical information, and final diagnoses were collected. After data preprocessing, a convolutional neural network (CNN) incorporating a channel attention mechanism was developed and trained. Model performance was assessed using accuracy, precision, recall, F1-score, area under the curve (AUC), and confusion matrices. Additionally, a blinded comparative evaluation was conducted against expert cardiologists. Results The model demonstrated excellent discriminative capability for ST-elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction (NSTEMI), with AUC values of 0.986 and 0.916, respectively. For STEMI, all performance metrics indicated superior diagnostic accuracy, and inference time was significantly shorter than manual interpretation (0.24 ± 0.08 s, p 0.001). However, detection performance for unstable angina (UA) and aortic dissection (AD) remained suboptimal, characterized by high sensitivity but relatively low precision. Conclusions The deep learning model based on 12-lead ECGs enables rapid and reliable detection of STEMI and NSTEMI, highlighting its potential as a valuable clinical decision-support tool in emergency department. Nevertheless, the recognition of UA and AD remains limited due to non-specific or transient electrophysiological features.
Luo et al. (Tue,) conducted a observational in acute chest pain (n=1,188). deep learning model based on 12-lead ECGs vs. physician interpretation was evaluated on diagnostic performance for STEMI and NSTEMI (null, 95% CI null, p=<0.001). The deep learning model achieved a diagnostic accuracy of 96.0% for STEMI and 89.1% for NSTEMI, significantly faster than physician interpretation (0.24 ± 0.08s vs. 23.84 ± 9.45s, p < 0.001).