Deep learning models may be able to extract further wall motion data from ECGs.

This study aimed to identify novel ECG features using deep learning to enhance WMA detection, referencing echocardiography as the gold standard. A deep neural network (ECG-WMA-Net) was trained and explainability analysis revealed significant contributions from QRS and T-wave regions in addition to Q waves. This deep learning approach improves WMA screening accuracy, potentially addressing physiological differences not captured by standard ECG-based methods.

https://www.nature.com/articles/s41746-024-01407-y