Artificial intelligence versus physicians on interpretation of printed ECG images: Diagnostic performance of ST-elevation myocardial infarction on electrocardiography. (15th September 2022)

• Record Type: Journal Article
• Authors: Choi, Yoo Jin; Park, Min Ji; Ko, Yura; Soh, Moon-Seung; Kim, Hyue Mee; Kim, Chee Hae; Lee, Eunkyoung; Kim, Joonghee
• Is Part Of: International journal of cardiology. Volume 363(2022)
• Page Start: 6
• Page End: 10
• Publication Date: 2022-09-15
• Subjects: Artificial intelligence -- ST-elevation myocardial infarction -- Electrocardiography -- Smartphone -- Emergency department; Cardiology -- Periodicals; Electronic journals; 616.12
• DOI: 10.1016/j.ijcard.2022.06.012
• Languages: English
• ISSNs: 0167-5273

Abstract

Abstract: Background: Smartphone-based ECG analyzer using camera input can be useful as everyone have it. The purpose of this study was to evaluate whether such a system can outperform clinicians in detecting ST-elevation myocardial infarction (STEMI) regardless of image acquisition conditions. Methods: We retrospectively enrolled suspected STEMI patients in an emergency department from January to October 2021. A multifaceted cardiovascular assessment system (Quantitative ECG, QCG™) using ECG images to produce a quantitative score (QCG score, ranging from 0 to 100) was compared to human experts of 7 emergency physicians and 3 cardiologists. Voting scores (number of participants answering "yes" for STEMI) were calculated for comparison. The system's robustness was evaluated using an equivalence test where we prove its performance metric (area under the curve of the receiver operating characteristic curve, AUC-ROC) changes within a predetermined equivalence range (−0.01 to 0.01) in 6 different environments (A combination of three different smartphones and two image sources including computer screen and paper). Results: 187 patients (96 STEMI, 51.3%) were analyzed. AUC-ROC of QCG score was 0.919 (0.880–0.957). AUC-ROCs of voting scores, 0.856 (0.799–0.913) for all clinicians, 0.843 (0.786–0.900) for emergency physicians, 0.817 (0.756–0.877) for cardiologists, and 0.848 (0.790–0.905) for high-performance group were significantly lower compared to that of QCG score. The change in AUC-ROC by image acquisition condition was negligible with a narrow confidence interval within −0.01 to 0.01 confirming the equivalence. Conclusions: Image-based AI system can outperform clinicians in STEMI diagnosis and its performance was robust to change in image acquisition conditions. Highlights: Image-based ECG AI has great potential as it can turn any smartphone into an ECG analyzer. Our image-based ECG analyzer showed super-human performance in detecting STEMI. The high performance was robust to change in image acquisition conditions.