Kenichi Harumi Plenary Address at Annual Meeting of the International Society of Computers in Electrocardiology: What Should ECG Deep Learning Focus on? The diagnosis of acute coronary occlusion!

According to the STEMI paradigm, only patients whose ECGs meet STEMI criteria require immediate reperfusion. This leads to reperfusion delays and significantly increases the mortality for the quarter of non-STEMI patients with totally occluded arteries. The Occlusion MI (OMI) paradigm has developed advanced ECG interpretation to identify this high-risk group, including examining the ECG in totality and assessing ST/T changes in proportion to the QRS. If neural networks are only developed based on STEMI databases and to identify STEMI criteria, they will simply reinforce a failed paradigm. But if deep learning is trained to identify OMI it could revolutionize patient care. This article reviews the paradigm shift from STEMI and OMI, and examines the potential and pitfalls of deep learning. This is based on the Kenichi Harumi Plenary Address at the Annual Meeting of the International Society of Computers in Electrocardiology, given by OMI expert Dr. Stephen Smith.

Automated summary

According to the STEMI paradigm, immediate reperfusion is only required for patients whose ECGs meet STEMI criteria, leading to reperfusion delays and increased mortality for non-STEMI patients. The OMI paradigm has advanced ECG interpretation to identify the high-risk group, assessing ST/T changes in proportion to the QRS. Developing neural networks based solely on STEMI databases would reinforce a failed paradigm, but training deep learning to identify OMI has the potential to revolutionize patient care. This article reviews the paradigm shift from STEMI to OMI and explores the potential and pitfalls of deep learning, based on a plenary address by OMI expert Dr. Stephen Smith at the International Society of Computers in Electrocardiology Annual Meeting.