Anne Kim, Mitchell Chatterjee, Alla Iansavitchene, Majid Komeili, Adrian D.C. Chan, Homer Yang, Jason Chui · British Journal of Anaesthesia 135(3): 561–570, 2025 · DOI 10.1016/j.bja.2025.05.037
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.
Cite#
@article{kim2025perioperative,
title = {Artificial intelligence for electrocardiographic diagnosis of perioperative myocardial ischaemia: a scoping review},
author = {Kim, Anne and Chatterjee, Mitchell and Iansavitchene, Alla and Komeili, Majid and Chan, Adrian D. C. and Yang, Homer and Chui, Jason},
journal = {British Journal of Anaesthesia},
volume = {135},
number = {3},
pages = {561--570},
year = {2025},
doi = {10.1016/j.bja.2025.05.037}
}Cover image: hospital operating room, public domain (CC0) via rawpixel.
