Deep learning analysis of exercise stress electrocardiography for identification of significant coronary artery disease
BackgroundThe diagnostic power of exercise stress electrocardiography (ExECG) remains limited. We aimed to construct an artificial intelligence (AI)-based method to enhance ExECG performance to identify patients with significant coronary artery disease (CAD).MethodsWe retrospectively collected 818 p...
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1496109/full |
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| author | Hsin-Yueh Liang Hsin-Yueh Liang Kai-Cheng Hsu Kai-Cheng Hsu Kai-Cheng Hsu Shang-Yu Chien Chen-Yu Yeh Ting-Hsuan Sun Meng-Hsuan Liu Kee Koon Ng |
| author_facet | Hsin-Yueh Liang Hsin-Yueh Liang Kai-Cheng Hsu Kai-Cheng Hsu Kai-Cheng Hsu Shang-Yu Chien Chen-Yu Yeh Ting-Hsuan Sun Meng-Hsuan Liu Kee Koon Ng |
| author_sort | Hsin-Yueh Liang |
| collection | DOAJ |
| description | BackgroundThe diagnostic power of exercise stress electrocardiography (ExECG) remains limited. We aimed to construct an artificial intelligence (AI)-based method to enhance ExECG performance to identify patients with significant coronary artery disease (CAD).MethodsWe retrospectively collected 818 patients who underwent both ExECG and coronary angiography (CAG) within 6 months. The mean age was 57.0 ± 10.1 years, and 614 (75%) were male patients. Significant coronary artery disease was seen in 369 (43.8%) CAG reports. We also included 197 individuals with normal ExECG and low risk of CAD. A convolutional recurrent neural network algorithm, integrating electrocardiographic (ECG) signals and features from ExECG reports, was developed to predict the risk of significant CAD. We also investigated the optimal number of inputted ECG signal slices and features and the weighting of features for model performance.ResultsUsing the data of patients undergoing CAG for training and test sets, our algorithm had an area under the curve, sensitivity, and specificity of 0.74, 0.86, and 0.47, respectively, which increased to 0.83, 0.89, and 0.60, respectively, after enrolling 197 subjects with low risk of CAD. Three ECG signal slices and 12 features yielded optimal performance metrics. The principal predictive feature variables were sex, maximum heart rate, and ST/HR index. Our model generated results within one minute after completing ExECG.ConclusionThe multimodal AI algorithm, leveraging deep learning techniques, efficiently and accurately identifies patients with significant CAD using ExECG data, aiding clinical screening in both symptomatic and asymptomatic patients. Nevertheless, the specificity remains moderate (0.60), suggesting a potential for false positives and highlighting the need for further investigation. |
| format | Article |
| id | doaj-art-d2f9461609ea4c49bfca238541f804e1 |
| institution | DOAJ |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-d2f9461609ea4c49bfca238541f804e12025-08-20T02:55:57ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-03-01810.3389/frai.2025.14961091496109Deep learning analysis of exercise stress electrocardiography for identification of significant coronary artery diseaseHsin-Yueh Liang0Hsin-Yueh Liang1Kai-Cheng Hsu2Kai-Cheng Hsu3Kai-Cheng Hsu4Shang-Yu Chien5Chen-Yu Yeh6Ting-Hsuan Sun7Meng-Hsuan Liu8Kee Koon Ng9Division of Cardiology, Department of Medicine, China Medical University Hospital, Taichung, TaiwanDepartment of Biomedical Imaging and Radiological Science, China Medical University, Taichung, TaiwanArtificial Intelligence Center, China Medical University Hospital, Taichung, TaiwanSchool of Medicine, China Medical University, Taichung, TaiwanDepartment of Neurology, China Medical University Hospital, Taichung, TaiwanArtificial Intelligence Center, China Medical University Hospital, Taichung, TaiwanArtificial Intelligence Center, China Medical University Hospital, Taichung, TaiwanArtificial Intelligence Center, China Medical University Hospital, Taichung, TaiwanArtificial Intelligence Center, China Medical University Hospital, Taichung, TaiwanDivision of Cardiology, Department of Medicine, China Medical University Hospital, Taichung, TaiwanBackgroundThe diagnostic power of exercise stress electrocardiography (ExECG) remains limited. We aimed to construct an artificial intelligence (AI)-based method to enhance ExECG performance to identify patients with significant coronary artery disease (CAD).MethodsWe retrospectively collected 818 patients who underwent both ExECG and coronary angiography (CAG) within 6 months. The mean age was 57.0 ± 10.1 years, and 614 (75%) were male patients. Significant coronary artery disease was seen in 369 (43.8%) CAG reports. We also included 197 individuals with normal ExECG and low risk of CAD. A convolutional recurrent neural network algorithm, integrating electrocardiographic (ECG) signals and features from ExECG reports, was developed to predict the risk of significant CAD. We also investigated the optimal number of inputted ECG signal slices and features and the weighting of features for model performance.ResultsUsing the data of patients undergoing CAG for training and test sets, our algorithm had an area under the curve, sensitivity, and specificity of 0.74, 0.86, and 0.47, respectively, which increased to 0.83, 0.89, and 0.60, respectively, after enrolling 197 subjects with low risk of CAD. Three ECG signal slices and 12 features yielded optimal performance metrics. The principal predictive feature variables were sex, maximum heart rate, and ST/HR index. Our model generated results within one minute after completing ExECG.ConclusionThe multimodal AI algorithm, leveraging deep learning techniques, efficiently and accurately identifies patients with significant CAD using ExECG data, aiding clinical screening in both symptomatic and asymptomatic patients. Nevertheless, the specificity remains moderate (0.60), suggesting a potential for false positives and highlighting the need for further investigation.https://www.frontiersin.org/articles/10.3389/frai.2025.1496109/fullexercise stress electrocardiographycoronary artery diseasedeep learningmultimodal approachfeature variableartificial intelligence |
| spellingShingle | Hsin-Yueh Liang Hsin-Yueh Liang Kai-Cheng Hsu Kai-Cheng Hsu Kai-Cheng Hsu Shang-Yu Chien Chen-Yu Yeh Ting-Hsuan Sun Meng-Hsuan Liu Kee Koon Ng Deep learning analysis of exercise stress electrocardiography for identification of significant coronary artery disease Frontiers in Artificial Intelligence exercise stress electrocardiography coronary artery disease deep learning multimodal approach feature variable artificial intelligence |
| title | Deep learning analysis of exercise stress electrocardiography for identification of significant coronary artery disease |
| title_full | Deep learning analysis of exercise stress electrocardiography for identification of significant coronary artery disease |
| title_fullStr | Deep learning analysis of exercise stress electrocardiography for identification of significant coronary artery disease |
| title_full_unstemmed | Deep learning analysis of exercise stress electrocardiography for identification of significant coronary artery disease |
| title_short | Deep learning analysis of exercise stress electrocardiography for identification of significant coronary artery disease |
| title_sort | deep learning analysis of exercise stress electrocardiography for identification of significant coronary artery disease |
| topic | exercise stress electrocardiography coronary artery disease deep learning multimodal approach feature variable artificial intelligence |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1496109/full |
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