Artificial intelligence-enhanced electrocardiography improves the detection of coronary artery disease
An AI-assisted algorithm has been developed to improve the detection of significant coronary artery disease (CAD) in high-risk individuals who have normal electrocardiograms (ECGs). This retrospective study analyzed ECGs from patients aged ≥ 18 years who were undergoing coronary angiography to obtai...
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| Language: | English |
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Elsevier
2025-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037024004550 |
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| author | Chi-Hsiao Yeh Tsung-Hsien Tsai Chun-Hung Chen Yi-Ju Chou Chun-Tai Mao Tzu-Pei Su Ning-I Yang Chi-Chun Lai Chien-Tzung Chen Huey-Kang Sytwu Ting-Fen Tsai |
| author_facet | Chi-Hsiao Yeh Tsung-Hsien Tsai Chun-Hung Chen Yi-Ju Chou Chun-Tai Mao Tzu-Pei Su Ning-I Yang Chi-Chun Lai Chien-Tzung Chen Huey-Kang Sytwu Ting-Fen Tsai |
| author_sort | Chi-Hsiao Yeh |
| collection | DOAJ |
| description | An AI-assisted algorithm has been developed to improve the detection of significant coronary artery disease (CAD) in high-risk individuals who have normal electrocardiograms (ECGs). This retrospective study analyzed ECGs from patients aged ≥ 18 years who were undergoing coronary angiography to obtain a clinical diagnosis at Chang Gung Memorial Hospital in Taiwan. Utilizing 12-lead ECG datasets, the algorithm integrated features like time intervals, amplitudes, and slope between peaks, a total of 561 features, with the XGBoost model yielding the best performance. The AI-enhanced ECG algorithm demonstrated high sensitivity (0.82–0.84) when detecting CAD in patients with normal ECGs and gave remarkably high prediction rates among those with abnormal ECGs, both with and without ischemia (92 %-95 % and 80 %-83 %, respectively). Notably, the algorithm's top features, mostly related to slope and amplitude differences, are challenging for clinicians to discern manually. Additionally, the study highlights significant sex differences regarding feature prediction and ranking. Comparatively, the AI-enhanced ECG's detection capability matched that of myocardial perfusion scintigraphy, which is a costly nuclear medicine test, and offers a more accessible alternative for identifying significant CAD, especially among patients with atypical ECG readings. |
| format | Article |
| id | doaj-art-47b678a70481495ea274bbec8a0d9bac |
| institution | Kabale University |
| issn | 2001-0370 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-47b678a70481495ea274bbec8a0d9bac2025-01-11T06:41:09ZengElsevierComputational and Structural Biotechnology Journal2001-03702025-01-0127278286Artificial intelligence-enhanced electrocardiography improves the detection of coronary artery diseaseChi-Hsiao Yeh0Tsung-Hsien Tsai1Chun-Hung Chen2Yi-Ju Chou3Chun-Tai Mao4Tzu-Pei Su5Ning-I Yang6Chi-Chun Lai7Chien-Tzung Chen8Huey-Kang Sytwu9Ting-Fen Tsai10Department of Thoracic and Cardiovascular Surgery, Chang Gung Memorial Hospital, Linkou, Taoyuan 333, Taiwan; Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung 204, Taiwan; College of Medicine, Chang Gung University, Taoyuan 333, TaiwanAdvanced Tech BU, Acer Inc., New Taipei City 221, TaiwanAdvanced Tech BU, Acer Inc., New Taipei City 221, TaiwanInstitute of Molecular and Genomic Medicine, National Health Research Institutes, Miaoli 350, TaiwanCommunity Medicine Research Center, Chang Gung Memorial Hospital, Keelung 204, Taiwan; College of Medicine, Chang Gung University, Taoyuan 333, Taiwan; Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Keelung 204, TaiwanCollege of Medicine, Chang Gung University, Taoyuan 333, Taiwan; Department of Nuclear Medicine, Chang Gung Memorial Hospital, Keelung 204, TaiwanCommunity Medicine Research Center, Chang Gung Memorial Hospital, Keelung 204, Taiwan; College of Medicine, Chang Gung University, Taoyuan 333, Taiwan; Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Keelung 204, TaiwanCommunity Medicine Research Center, Chang Gung Memorial Hospital, Keelung 204, Taiwan; College of Medicine, Chang Gung University, Taoyuan 333, Taiwan; Department of Ophthalmology, Chang Gung Memorial Hospital, Keelung 204, TaiwanCollege of Medicine, Chang Gung University, Taoyuan 333, Taiwan; Department of Plastic & Reconstructive Surgery, Chang Gung Memorial Hospital, Linkou, Taoyuan 333, Taiwan; Correspondence to: No 155, Section 2, Li-Nong St, Beitou Dist., Taipei 112304, Taiwan.National Institute of Infectious Diseases and Vaccinology, National Health Research Institutes, Miaoli 350, Taiwan; Department & Graduate Institute of Microbiology and Immunology, National Defense Medical Center, Taipei 114, Taiwan; Correspondence to: No 155, Section 2, Li-Nong St, Beitou Dist., Taipei 112304, Taiwan.Institute of Molecular and Genomic Medicine, National Health Research Institutes, Miaoli 350, Taiwan; Department of Life Sciences and Institute of Genome Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan; Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan; Correspondence to: No 155, Section 2, Li-Nong St, Beitou Dist., Taipei 112304, Taiwan.An AI-assisted algorithm has been developed to improve the detection of significant coronary artery disease (CAD) in high-risk individuals who have normal electrocardiograms (ECGs). This retrospective study analyzed ECGs from patients aged ≥ 18 years who were undergoing coronary angiography to obtain a clinical diagnosis at Chang Gung Memorial Hospital in Taiwan. Utilizing 12-lead ECG datasets, the algorithm integrated features like time intervals, amplitudes, and slope between peaks, a total of 561 features, with the XGBoost model yielding the best performance. The AI-enhanced ECG algorithm demonstrated high sensitivity (0.82–0.84) when detecting CAD in patients with normal ECGs and gave remarkably high prediction rates among those with abnormal ECGs, both with and without ischemia (92 %-95 % and 80 %-83 %, respectively). Notably, the algorithm's top features, mostly related to slope and amplitude differences, are challenging for clinicians to discern manually. Additionally, the study highlights significant sex differences regarding feature prediction and ranking. Comparatively, the AI-enhanced ECG's detection capability matched that of myocardial perfusion scintigraphy, which is a costly nuclear medicine test, and offers a more accessible alternative for identifying significant CAD, especially among patients with atypical ECG readings.http://www.sciencedirect.com/science/article/pii/S2001037024004550ElectrocardiogramsCoronary artery diseaseArtificial intelligence |
| spellingShingle | Chi-Hsiao Yeh Tsung-Hsien Tsai Chun-Hung Chen Yi-Ju Chou Chun-Tai Mao Tzu-Pei Su Ning-I Yang Chi-Chun Lai Chien-Tzung Chen Huey-Kang Sytwu Ting-Fen Tsai Artificial intelligence-enhanced electrocardiography improves the detection of coronary artery disease Computational and Structural Biotechnology Journal Electrocardiograms Coronary artery disease Artificial intelligence |
| title | Artificial intelligence-enhanced electrocardiography improves the detection of coronary artery disease |
| title_full | Artificial intelligence-enhanced electrocardiography improves the detection of coronary artery disease |
| title_fullStr | Artificial intelligence-enhanced electrocardiography improves the detection of coronary artery disease |
| title_full_unstemmed | Artificial intelligence-enhanced electrocardiography improves the detection of coronary artery disease |
| title_short | Artificial intelligence-enhanced electrocardiography improves the detection of coronary artery disease |
| title_sort | artificial intelligence enhanced electrocardiography improves the detection of coronary artery disease |
| topic | Electrocardiograms Coronary artery disease Artificial intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S2001037024004550 |
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