An Artificial Intelligence-Enabled ECG Algorithm for the Prediction and Localization of Angiography-Proven Coronary Artery Disease
(1) Background: The role of using artificial intelligence (AI) with electrocardiograms (ECGs) for the diagnosis of significant coronary artery disease (CAD) is unknown. We first tested the hypothesis that using AI to read ECG could identify significant CAD and determine which vessel was obstructed....
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| Format: | Article |
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MDPI AG
2022-02-01
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| Series: | Biomedicines |
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| Online Access: | https://www.mdpi.com/2227-9059/10/2/394 |
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| author | Pang-Shuo Huang Yu-Heng Tseng Chin-Feng Tsai Jien-Jiun Chen Shao-Chi Yang Fu-Chun Chiu Zheng-Wei Chen Juey-Jen Hwang Eric Y. Chuang Yi-Chih Wang Chia-Ti Tsai |
| author_facet | Pang-Shuo Huang Yu-Heng Tseng Chin-Feng Tsai Jien-Jiun Chen Shao-Chi Yang Fu-Chun Chiu Zheng-Wei Chen Juey-Jen Hwang Eric Y. Chuang Yi-Chih Wang Chia-Ti Tsai |
| author_sort | Pang-Shuo Huang |
| collection | DOAJ |
| description | (1) Background: The role of using artificial intelligence (AI) with electrocardiograms (ECGs) for the diagnosis of significant coronary artery disease (CAD) is unknown. We first tested the hypothesis that using AI to read ECG could identify significant CAD and determine which vessel was obstructed. (2) Methods: We collected ECG data from a multi-center retrospective cohort with patients of significant CAD documented by invasive coronary angiography and control patients in Taiwan from 1 January 2018 to 31 December 2020. (3) Results: We trained convolutional neural networks (CNN) models to identify patients with significant CAD (>70% stenosis), using the 12,954 ECG from 2303 patients with CAD and 2090 ECG from 1053 patients without CAD. The Marco-average area under the ROC curve (AUC) for detecting CAD was 0.869 for image input CNN model. For detecting individual coronary artery obstruction, the AUC was 0.885 for left anterior descending artery, 0.776 for right coronary artery, and 0.816 for left circumflex artery obstruction, and 1.0 for no coronary artery obstruction. Marco-average AUC increased up to 0.973 if ECG had features of myocardial ischemia. (4) Conclusions: We for the first time show that using the AI-enhanced CNN model to read standard 12-lead ECG permits ECG to serve as a powerful screening tool to identify significant CAD and localize the coronary obstruction. It could be easily implemented in health check-ups with asymptomatic patients and identifying high-risk patients for future coronary events. |
| first_indexed | 2024-03-09T22:31:58Z |
| format | Article |
| id | doaj.art-75a2f8f99c8f4da38f124ab0a75ec114 |
| institution | Directory Open Access Journal |
| issn | 2227-9059 |
| language | English |
| last_indexed | 2024-03-09T22:31:58Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomedicines |
| spelling | doaj.art-75a2f8f99c8f4da38f124ab0a75ec1142023-11-23T18:54:54ZengMDPI AGBiomedicines2227-90592022-02-0110239410.3390/biomedicines10020394An Artificial Intelligence-Enabled ECG Algorithm for the Prediction and Localization of Angiography-Proven Coronary Artery DiseasePang-Shuo Huang0Yu-Heng Tseng1Chin-Feng Tsai2Jien-Jiun Chen3Shao-Chi Yang4Fu-Chun Chiu5Zheng-Wei Chen6Juey-Jen Hwang7Eric Y. Chuang8Yi-Chih Wang9Chia-Ti Tsai10Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital Yun-Lin Branch, Yunlin County 640, TaiwanGraduated Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, TaiwanDivision of Cardiology, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung 402, TaiwanDivision of Cardiology, Department of Internal Medicine, National Taiwan University Hospital Yun-Lin Branch, Yunlin County 640, TaiwanDivision of Cardiology, Department of Internal Medicine, National Taiwan University Hospital Yun-Lin Branch, Yunlin County 640, TaiwanDivision of Cardiology, Department of Internal Medicine, National Taiwan University Hospital Yun-Lin Branch, Yunlin County 640, TaiwanDivision of Cardiology, Department of Internal Medicine, National Taiwan University Hospital Yun-Lin Branch, Yunlin County 640, TaiwanCardiovascular Center, National Taiwan University Hospital, Taipei 100, TaiwanGraduated Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, TaiwanCardiovascular Center, National Taiwan University Hospital, Taipei 100, TaiwanCardiovascular Center, National Taiwan University Hospital, Taipei 100, Taiwan(1) Background: The role of using artificial intelligence (AI) with electrocardiograms (ECGs) for the diagnosis of significant coronary artery disease (CAD) is unknown. We first tested the hypothesis that using AI to read ECG could identify significant CAD and determine which vessel was obstructed. (2) Methods: We collected ECG data from a multi-center retrospective cohort with patients of significant CAD documented by invasive coronary angiography and control patients in Taiwan from 1 January 2018 to 31 December 2020. (3) Results: We trained convolutional neural networks (CNN) models to identify patients with significant CAD (>70% stenosis), using the 12,954 ECG from 2303 patients with CAD and 2090 ECG from 1053 patients without CAD. The Marco-average area under the ROC curve (AUC) for detecting CAD was 0.869 for image input CNN model. For detecting individual coronary artery obstruction, the AUC was 0.885 for left anterior descending artery, 0.776 for right coronary artery, and 0.816 for left circumflex artery obstruction, and 1.0 for no coronary artery obstruction. Marco-average AUC increased up to 0.973 if ECG had features of myocardial ischemia. (4) Conclusions: We for the first time show that using the AI-enhanced CNN model to read standard 12-lead ECG permits ECG to serve as a powerful screening tool to identify significant CAD and localize the coronary obstruction. It could be easily implemented in health check-ups with asymptomatic patients and identifying high-risk patients for future coronary events.https://www.mdpi.com/2227-9059/10/2/394artificial intelligencedeep learningconvolutional neural networkcoronary artery disease |
| spellingShingle | Pang-Shuo Huang Yu-Heng Tseng Chin-Feng Tsai Jien-Jiun Chen Shao-Chi Yang Fu-Chun Chiu Zheng-Wei Chen Juey-Jen Hwang Eric Y. Chuang Yi-Chih Wang Chia-Ti Tsai An Artificial Intelligence-Enabled ECG Algorithm for the Prediction and Localization of Angiography-Proven Coronary Artery Disease Biomedicines artificial intelligence deep learning convolutional neural network coronary artery disease |
| title | An Artificial Intelligence-Enabled ECG Algorithm for the Prediction and Localization of Angiography-Proven Coronary Artery Disease |
| title_full | An Artificial Intelligence-Enabled ECG Algorithm for the Prediction and Localization of Angiography-Proven Coronary Artery Disease |
| title_fullStr | An Artificial Intelligence-Enabled ECG Algorithm for the Prediction and Localization of Angiography-Proven Coronary Artery Disease |
| title_full_unstemmed | An Artificial Intelligence-Enabled ECG Algorithm for the Prediction and Localization of Angiography-Proven Coronary Artery Disease |
| title_short | An Artificial Intelligence-Enabled ECG Algorithm for the Prediction and Localization of Angiography-Proven Coronary Artery Disease |
| title_sort | artificial intelligence enabled ecg algorithm for the prediction and localization of angiography proven coronary artery disease |
| topic | artificial intelligence deep learning convolutional neural network coronary artery disease |
| url | https://www.mdpi.com/2227-9059/10/2/394 |
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