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

A Deep-Learning Algorithm for Detecting Acute Myocardial Infarction

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. Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C.
5. Graduate Institute of Life Sciences, National Defense Medical Center, Taipei, Taiwan, R.O.C.
6. Planning and Management Office, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C; Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.
7. Graduate Institute of Life Sciences, National Defense Medical Center, Taipei, Taiwan, Taiwan
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Background

Delayed or misdiagnosis of acute myocardial infarction (AMI) is not unusual in the daily practice. Since 12- lead electrocardiogram (ECG) is crucial for the detection of AMI, the 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 not-AMI patients at the emergency department. The DLM was trained and validated by 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).

Conclusion

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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