Deep learning model may help detect type 1 MI and revascularization need from ECG patterns, reveals research

The model outperformed both traditional cardiac TnT (AUROC 0.71) and physician ECG interpretation in the test group, achieving an AUROC of 0.91. ECG model AUROC was 0.81 for revascularization and 0.85 for type 1 MI in the external validation cohort, while it was 0.67 and 0.74 for clinician interpretation and 0.85 and 0.87 for high-sensitivity (hs)-TnT, respectively. When compared to hs-TnT, the ECG model showed a lower sensitivity but a greater specificity.

The algorithm performed better than traditional hs-TnT testing and physicians' interpretation of the ECG in determining which individuals in the test population needed revascularization. Similar outcomes were obtained in the external validation cohort, where the ECG-AI model outperformed doctors in terms of interpretation but fell short of hs-TnT's predictive capabilities.

Overall, nearly 2,00,000 ECGs from two worldwide cohorts of patients with NSTE ACS who presented to the emergency department (ED) were used to train the deep-learning model. The findings imply that doctors may eventually get AI assistance when transferring suitable patients from the ED to the cath lab.

Reference:

Büscher, A., Plagwitz, L., Yildirim, K., Brix, T. J., Neuhaus, P., Bickmann, L., Menke, A. F., van Almsick, V. F., Pavenstädt, H., Kümpers, P., Heider, D., Varghese, J., & Eckardt, L. (2025). Deep learning electrocardiogram model for risk stratification of coronary revascularization need in the emergency department. European Heart Journal. https://doi.org/10.1093/eurheartj/ehaf254