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

Abstract

ObjectiveTo implement an all-day online artificial intelligence (AI)-assisted detection of ST-elevation myocardial infarction (STEMI) by prehospital 12-lead electrocardiograms (ECGs) to facilitate patient triage for timely reperfusion therapy.MethodsThe proposed AI model combines a convolutional neural network and long short-term memory (CNN-LSTM) to predict STEMI on prehospital 12-lead ECGs obtained from mini-12-lead ECG devices equipped in ambulance vehicles in Central Taiwan. Emergency medical technicians (EMTs) from the 14 AI-implemented fire stations performed the on-site 12-lead ECG examinations using the mini portable device. The 12-lead ECG signals were transmitted to the AI center of China Medical University Hospital to classify the recordings as “STEMI” or “Not STEMI”. In 11 non-AI fire stations, the ECG data were transmitted to a secure network and read by available on-line emergency physicians. The response time was defined as the time interval between the ECG transmission and ECG interpretation feedback.ResultsBetween July 17, 2021, and March 26, 2022, the AI model classified 362 prehospital 12-lead ECGs obtained from 275 consecutive patients who had called the 119 dispatch centers of fire stations in Central Taiwan for symptoms of chest pain or shortness of breath. The AI's response time to the EMTs in ambulance vehicles was 37.2 ± 11.3 s, which was shorter than the online physicians' response time from 11 other fire stations with no AI implementation (113.2 ± 369.4 s, P < 0.001) after analyzing another set of 335 prehospital 12-lead ECGs. The evaluation metrics including accuracy, precision, specificity, recall, area under the receiver operating characteristic curve, and F1 score to assess the overall AI performance in the remote detection of STEMI were 0.992, 0.889, 0.994, 0.941, 0.997, and 0.914, respectively. During the study period, the AI model promptly identified 10 STEMI patients who underwent primary percutaneous coronary intervention (PPCI) with a median contact-to-door time of 18.5 (IQR: 16–20.8) minutes.ConclusionImplementation of an all-day real-time AI-assisted remote detection of STEMI on prehospital 12-lead ECGs in the field is feasible with a high diagnostic accuracy rate. This approach may help minimize preventable delays in contact-to-treatment times for STEMI patients who require PPCI.

Artificial intelligence in critical illness and its impact on patient care: a comprehensive review

“…If they prove successful, it could significantly increase the efficiency of care delivery in the ICU. One-size-fits-all solutions are not effective in dealing with complex problems, as evidenced by the lack of improvement in septic shock outcomes in recent years despite various treatment guidelines (19,20) 22) reported using AI-assisted real-time analysis of electrocardiograms in the prehospital setting and found that it was feasible and had the potential to reduce delays in treatment times for patients requiring percutaneous coronary interventions (22). These examples demonstrate the use of AI for therapeutic guidance in medical decision-making for critically ill patients with good efficacy.…”

“…AI can also perform real-time electrocardiogram analysis to detect myocardial infarctions. A study by Chen et al ( 22 ) reported using AI-assisted real-time analysis of electrocardiograms in the prehospital setting and found that it was feasible and had the potential to reduce delays in treatment times for patients requiring percutaneous coronary interventions ( 22 ). These examples demonstrate the use of AI for therapeutic guidance in medical decision-making for critically ill patients with good efficacy.…”

“…By assisting in decision-making and enabling healthcare professionals to concentrate their efforts on investing more time with patients, artificial intelligence can help to promote shared decision-making (SDM) ( 22 ). AI technologies offer a wide range of information and have the capacity to evaluate enormous amounts of data and find correlations that scholars and healthcare professionals would have overlooked ( 23 ).…”

A Novel Algorithm for Improving the Prehospital Diagnostic Accuracy of ST-Segment Elevation Myocardial Infarction

“…New software interpretation that utilizes machine learning may have entirely different sources of errors or be so accurate as to obviate the incremental gains from an approach such as this. 45,46…”

“…New software interpretation that utilizes machine learning may have entirely different sources of errors or be so accurate as to obviate the incremental gains from an approach such as this. 45,46 Previous studies have used a variety of gold standards for true STEMI, including physician consensus of ECG findings, disposition to CCL, cardiac biomarkers, and CCL outcomes, which makes comparison difficult. The authors used appropriate CCL activation, determined based on a number of outcomes as well as cardiologist consensus.…”