A deep learning algorithm for detecting acute myocardial infarction

Abstract

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.

Figure 1

Performance comparison for STEMI detection in the human-machine competition. The area under the receiver operating characteristic curve (AUC) was generated by the prediction of the DLM. The triangles, the square and the diamond denote the cardiologists, the emergency physician and the Philips algorithm, respectively. A) The ROC curve in the competition set (STEMI=174, NSTEMI=138, and non-AMI=138). B) The precision-recall ROC curve in the revised proportion of the hypothetical real world (STEMI=0.1%, NSTEMI=0.2%, and non-AMI=99.7%).

Figure 2

Performance rankings and consistency analysis of STEMI detection among the DLM, the physicians and the Philips algorithm in the human-machine competition. A) Global performance rankings based on the class-3 kappa values. V(X) denotes (V) visiting staff with (X) years of experience. B) Consistency analysis as a heatmap coloured based on the values; the values in each cell were the kappa values of each pair.

Figure 3

Interpretations of selected STEMI ECGs by the DLM and physicians in the human-machine competition. A) Both the DLM and the best cardiologists consistently detected STEMI. B) The DLM misdetected STEMI, which was correctly detected by the best cardiologists. C) Both the DLM and the best cardiologists misdetected STEMI. D) The DLM correctly detected STEMI, which was misdetected by the best cardiologists.

Figure 4

Comparison of the diagnostic value between the DLM and cTnI in the validation cohort. The area under the receiver operating characteristic curve (AUC) was generated from the logistic regression analysis using the validation cohort. The p-values represent the comparison among the DLM, cTnI and the DLM plus cTnI. A) Regarding STEMI detection: DLM vs cTnI, p<0.001; DLM vs DLM+cTnI, p=ns. B) Regarding NSTEMI detection: cTnI vs DLM, p<0.01; cTnI+DLM vs cTnI, p<0.05.

Central illustration

Schematic diagram of the development, validation and future application of the current deep learning model for detecting AMI. The DLM learned from more than 100,000 ECGs was developed and trained. Compared with cardiologist-level physicians. The DLM exhibited the best performance in the detection of STEMI. The validated model achieved excellent diagnostic power with a sensitivity of 98.4% and a specificity of 96.9% for STEMI detection. With the ability of real-time detection, precise diagnosis and early alarm, the application of DLM for STEMI detection, including in-hospital, pre-hospital settings, telemedicine and wearable devices, would improve the quality of health care of cardiovascular disease in the near future.