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!”
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".