Evidence map›Paper›PMID 40615329›Full record

ArticleBritish journal of anaesthesia2025

Artificial intelligence for electrocardiographic diagnosis of perioperative myocardial ischaemia: a scoping review.

Anne Kim, Mitchell Chatterjee, Alla Iansavitchene, Majid Komeili, Adrian D C Chan, Homer Yang, Jason Chui

Abstract readScoping Review
PubMed Full text at PMC Publisher
In one paragraph

Article in British journal of anaesthesia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Anne KimSchulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada.
Mitchell ChatterjeeDepartment of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
Alla IansavitcheneLondon Health Science Centre, London, ON, Canada.
Majid KomeiliDepartment of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
Adrian D C ChanDepartment of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
Homer YangDepartment of Anesthesia & Perioperative Medicine, University of Western Ontario, London, ON, Canada.
Jason ChuiDepartment of Anesthesia & Perioperative Medicine, University of Western Ontario, London, ON, Canada. Electronic address: Jason.chui@lhsc.on.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPerioperative 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.

methodsA 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.

resultsA 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.

conclusionsAI 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.

Indexed as

Artificial IntelligenceElectrocardiographyMyocardial IschemiaPerioperative CareAlgorithmsHumansartificial intelligenceECGelectrocardiographymachine learningmonitoringmyocardial infarctionmyocardial ischaemiaremote home monitoring

Identifiers

PMID40615329
PMCPMC12489347

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.