Electrocardiogram-based deep learning algorithm for the screening of obstructive coronary artery disease

BMC Cardiovascular Disorders

Research

Tae-Hun Kim¹  Seong Huan Choi²  Sang-Don Park²  Won Kyung Lee³  Woojoo Lee⁴  Jang-Whan Bae⁵  Mi-Sook Kim⁶  Hyun-Gye Lee⁷ 

[1] Department of Artificial Intelligence, Inha University, Incheon, Korea;Department of Cardiology, School of Medicine, Inha University Hospital, Inha University, Incheon, Korea;Department of Prevention and Management, School of Medicine, Inha University Hospital, Inha University, 27 Inhang-Ro, Jung-Gu, Incheon, Korea;Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Korea;Division of Cardiology, Department of Internal Medicine, Chungbuk National University College of Medicine, Cheongju, Korea;Division of Clinical Epidemiology, Medical Research Collaborating Center, Biomedical Research Institution, Seoul National University Hospital, Seoul, Korea;School of Medicine, Inha University, Incheon, Korea;

关键词: Obstructive coronary artery disease;    Acute myocardial infarction;    Deep learning;    Convolutional neural network;    Electrocardiogram;   

DOI  :  10.1186/s12872-023-03326-4

 received in 2022-11-18, accepted in 2023-05-30,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundInformation on electrocardiogram (ECG) has not been quantified in obstructive coronary artery disease (ObCAD), despite the deep learning (DL) algorithm being proposed as an effective diagnostic tool for acute myocardial infarction (AMI). Therefore, this study adopted a DL algorithm to suggest the screening of ObCAD from ECG.MethodsECG voltage-time traces within a week from coronary angiography (CAG) were extracted for the patients who received CAG for suspected CAD in a single tertiary hospital from 2008 to 2020. After separating the AMI group, those were classified into ObCAD and non-ObCAD groups based on the CAG results. A DL-based model adopting ResNet was built to extract information from ECG data in the patients with ObCAD relative to those with non-ObCAD, and compared the performance with AMI. Moreover, subgroup analysis was conducted using ECG patterns of computer-assisted ECG interpretation.ResultsThe DL model demonstrated modest performance in suggesting the probability of ObCAD but excellent performance in detecting AMI. The AUC of the ObCAD model adopting 1D ResNet was 0.693 and 0.923 in detecting AMI. The accuracy, sensitivity, specificity, and F1 score of the DL model for screening ObCAD were 0.638, 0.639, 0.636, and 0.634, respectively, while the figures were up to 0.885, 0.769, 0.921, and 0.758 for detecting AMI, respectively. Subgroup analysis showed that the difference between normal and abnormal/borderline ECG groups was not notable.ConclusionsECG-based DL model showed fair performance for assessing ObCAD and it may serve as an adjunct to the pre-test probability in patients with suspected ObCAD during the initial evaluation. With further refinement and evaluation, ECG coupled with the DL algorithm may provide potential front-line screening support in the resource-intensive diagnostic pathways.

【 授权许可】

CC BY   
© The Author(s) 2023

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