The Official Journal of EuroPCR and the European Association of Percutaneous Coronary Interventions (EAPCI)

Coronary interventions

A deep learning algorithm for detecting acute myocardial infarction

EuroIntervention 2021;17:765-773. DOI: 10.4244/EIJ-D-20-01155

1. Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C.; 2. Division of Cardiovascular Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C.; 3. Division of Cardiology, Heart Centre, Cheng Hsin Hospital, Taipei, Taiwan, R.O.C.; 4. Graduate Institute of Life Sciences, National Defense Medical Center, Taipei, Taiwan, R.O.C.; 5. Planning and Management Office, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C; 6. Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C.; 7. School of Medicine, National Defense Medical Center, Taipei, Taiwan, R.O.C.; 8. School of Public Health, National Defense Medical Center, Taipei, Taiwan, R.O.C.

Background: Delayed diagnosis or misdiagnosis of acute myocardial infarction (AMI) is not unusual in daily practice. Since a 12-lead electrocardiogram (ECG) is crucial for the detection of AMI, a systematic algorithm to strengthen ECG interpretation may have important implications for improving diagnosis.

Aims: We aimed to develop a deep learning model (DLM) as a diagnostic support tool based on a 12-lead electrocardiogram.

Methods: This retrospective cohort study included 1,051/697 ECGs from 737/287 coronary angiogram (CAG)-validated STEMI/NSTEMI patients and 140,336 ECGs from 76,775 non-AMI patients at the emergency department. The DLM was trained and validated in 80% and 20% of these ECGs. A human-machine competition was conducted. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the performance of the DLM.

Results: The AUC of the DLM for STEMI detection was 0.976 in the human-machine competition, which was significantly better than that of the best physicians. Furthermore, the DLM independently demonstrated sufficient diagnostic capacity for STEMI detection (AUC=0.997; sensitivity, 98.4%; specificity, 96.9%). Regarding NSTEMI detection, the AUC of the combined DLM and conventional cardiac troponin I (cTnI) increased to 0.978, which was better than that of either the DLM (0.877) or cTnI (0.950).

Conclusions: The DLM may serve as a timely, objective and precise diagnostic decision support tool to assist emergency medical system-based networks and frontline physicians in detecting AMI and subsequently initiating reperfusion therapy.

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