Usefulness of multi-labelling artificial intelligence in detecting rhythm disorders and acute ST-elevation myocardial infarction on 12-lead electrocardiogram

Abstract

Aim To develop an artificial intelligence-based approach with multi-labeling capability to identify both ST-elevation myocardial infarction (STEMI) and 12 heart rhythms based on 12-lead ECGs. Methods We trained, validated, and tested a long short-term memory (LSTM) model for the multi-label diagnosis of 13 ECG patterns (STEMI+12 rhythm classes) using 60,537 clinical ECGs from 35,981 patients recorded between Jan 15, 2009 and Dec 31, 2018. In addition to the internal test above, we conducted a real-world external test, comparing the LSTM model with board-certified physicians of different specialties using a separate dataset of 308 ECGs covering all 13 ECG diagnoses. Results In the internal test, the area under curves (AUCs) of the LSTM model in classifying the 13 ECG patterns ranged between 0.939 and 0.999. For the external test, the LSTM model for multi-labeling of the 13 ECG patterns evaluated by AUC was 0.987±0.021, which was superior to those of cardiologists (0.898±0.113, P < 0.001), emergency physicians (0.820±0.134, P < 0.001), internists (0.765±0.155, P < 0.001), and a commercial algorithm (0.845±0.121, P < 0.001). Of note, the LSTM model achieved an accuracy of 0.987, AUC of 0.997, and precision, recall, and F 1 score of 0.952, 0.870, and 0.909, respectively, in detecting STEMI. Conclusions We demonstrated the usefulness of an LSTM model in the multi-labeling detection of both rhythm classes and STEMI in competitive testing against board-certified physicians. This AI tool exceeding the cardiologist-level performance in detecting STEMI and rhythm classes on 12-lead ECG may be useful in prioritizing chest pain triage and expediting clinical decision making in healthcare.

Artificial intelligence-assisted remote detection of ST-elevation myocardial infarction using a mini-12-lead electrocardiogram device in prehospital ambulance care

“…The development of our deep learning model to classify STEMI on 12-lead ECGs has been previously reported (4,5). Briefly, we first retrieved 3,296 12-lead ECG data from the digital ECG core laboratory database at the China Medical University Hospital (CMUH) in an extensible markup language (XML) format as inputs to develop the AI model.…”

“…Using on-site ECG transmission via the emergency medical system (EMS), STEMI patients could be transferred to the nearest interventional center by ambulance, bypassing the emergency department, and undergo timely catheter-based reperfusion therapy upon prehospital STEMI diagnosis. To improve the efficiency and accuracy of prehospital 12-lead ECG diagnosis, new technologies, including machine learning and deep learning algorithms, have been implemented (3)(4)(5).…”

“…The application of machine learning or deep learning algorithms to predict STEMI has recently gained considerable attention (4,6,7). One of the challenges in the development of such models is the lack of robust 12-lead STEMI datasets for training and validation.…”

“…We previously utilized a bidirectional long short-term memory deep learning model to detect STEMI and 12 major heart rhythms, which outperformed board-certified physicians including cardiologists, emergency physicians, and internists (4,6). We further developed a new model combining a convolutional neural network and long shortterm memory (CNN-LSTM), and implemented an all-day artificial intelligence (AI)-based triage system to facilitate STEMI ECG diagnosis in the ED (5).…”

Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review

“…A neural network (NN) is one way to train a computer to mimic the human brain, which enables the computer system to achieve AI through deep learning (DL). In recent years, there has been a surge in research publications where AI has been used in medicine and healthcare [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 ,…”