Open AccessArtificial intelligence to detect abnormal heart rhythm from scanned electrocardiogram tracings
Author(s) -
Bridge Joshua,
Fu Lu,
Lin Weidong,
Xue Yumei,
Lip Gregory Y. H.,
Zheng Yalin
Publication year - 2022
Publication title -
journal of arrhythmia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.463
H-Index - 21
eISSN - 1883-2148
pISSN - 1880-4276
DOI - 10.1002/joa3.12707
Subject(s) - medicine , sinus rhythm , normal sinus rhythm , cardiology , electrocardiography , confidence interval , artificial intelligence , atrial fibrillation , computer science
Background Electrocardiogram (ECG) interpretation is an integral part of the clinical ECG workflow; however, this process is often time‐consuming and labor‐intensive. We aim to develop a rapid, inexpensive means to detect abnormal ECGs using artificial intelligence (AI) from scanned ECG printouts. Methods The study included 1172 12‐lead ECG scans performed in 1172 individuals from a community in Guangzhou, China; 878 (74.9%) were diagnosed with sinus rhythm, and the remaining 294 (25.1%) with abnormal rhythms. A deep learning model consisting of a convolutional neural network based on InceptionV3 and a fully connected layer followed by a GEV activation was trained to classify scanned tracings as either normal or abnormal. Results In a hold‐out testing set, the model achieved a area under curve (AUC), sensitivity, specificity, PPV, and NPV of 0.932 (95% confidence interval [CI]: 0.890, 0.976), 0.816 (95% CI: 0.657, 0.923), 0.993 (95% CI: 0.959, 1.0), 0.969 (95% CI: 0.838, 0.999), and 0.950 (95% CI: 0.90, 0.980) respectively, when using a probability threshold of 0.5. When compared with a physiological expert, these results show comparable performance with a statistically significant increase in specificity and a non‐significant decrease in sensitivity at the 95% level. Conclusions We have developed a rapid, inexpensive, accurate means to detect abnormal ECGs using AI. Easy and accurate identification of such “abnormal” ECGs could allow the mass automated review of ECGs in community settings where abnormal ones could be flagged using AI for detailed clinical review by healthcare professionals.