Artificial intelligence for electrocardiographic diagnosis of perioperative myocardial ischaemia: a scoping review
Bibliographic record
Abstract
BACKGROUND: Perioperative electrocardiographic monitoring can offer immediate detection of myocardial ischaemia, yet its application in perioperative and remote monitoring settings is hampered by frequent false alarms and signal contamination. We performed a scoping review for the current state of artificial intelligence (AI) in perioperative ECG interpretation. METHODS: A literature search in Ovid MEDLINE, EMBASE, Compendex, and CINAHL databases was performed from inception to May 10, 2023. All original research of ECG monitoring for myocardial ischaemia, myocardial infarction, or both was included. RESULTS: A total of 182 original research articles published between 1991 and 2023 were included. Most studies (n=132) used a pre-existing ECG database to develop AI algorithms retrospectively, and the rest did not specify their sources. Processing filters were used in 58% of the studies to remove ECG noises/artifacts before AI algorithm development. Amongst the AI technologies used, ResNet demonstrated the highest median sensitivity, precision, and specificity at 98.4%, 99.8%, and 99.1%, respectively. There are only five studies with intermittent prospective ECG collection on ST-segment elevation myocardial infarction. No studies prospectively collected continuous ECG perioperatively, with frequent false alarms and signal contamination. CONCLUSIONS: AI technology can achieve high diagnostic accuracy for myocardial ischaemia detection in clean intermittent electrocardiograms. However, almost all these algorithms were developed from a few open-source clean ECG databases without testing on 'noisy data', which limited their clinical applicability in the perioperative setting where signal contamination is frequent. AI algorithms on perioperative electrocardiography, tested in a noisy perioperative and remote monitoring environment, including wearable devices, are needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".