Application of Artificial Intelligence Technologies in a Cloud-based Platform for ECG Analysis to Support the Diagnosis of a Critical Electrocardiography in Primary Care
BackgroundThe cloud-based platform for electrocardiography (ECG) analysis plays a supporting role in the prevention and treatment of cardiovascular diseases. During the construction of a cloud-based platform for ECG analysis, problems that should be focused and addressed are exploring ways to better...
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| Format: | Article |
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Chinese General Practice Publishing House Co., Ltd
2022-04-01
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| Series: | Zhongguo quanke yixue |
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| Online Access: | https://www.chinagp.net/fileup/1007-9572/PDF/1648435464222-1854732785.pdf |
| _version_ | 1797217259342528512 |
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| author | YU Xinyan, GU Zhile, ZHANG Xiaojuan, ZHAO Xiaoye, ZHANG Haicheng |
| author_facet | YU Xinyan, GU Zhile, ZHANG Xiaojuan, ZHAO Xiaoye, ZHANG Haicheng |
| author_sort | YU Xinyan, GU Zhile, ZHANG Xiaojuan, ZHAO Xiaoye, ZHANG Haicheng |
| collection | DOAJ |
| description | BackgroundThe cloud-based platform for electrocardiography (ECG) analysis plays a supporting role in the prevention and treatment of cardiovascular diseases. During the construction of a cloud-based platform for ECG analysis, problems that should be focused and addressed are exploring ways to better use artificial intelligence (AI) technologies supporting ECG analysis, and improving the process and effectiveness of AI-aided diagnosis of a critical ECG.ObjectiveTo explore the use of AI technologies in a cloud-based platform for ECG analysis to support the diagnosis of a critical ECG in primary care.MethodsThe 12-lead resting ECGs (n=20 808) uploaded to Nalong Cloud-based ECG Analysis Platform by primary healthcare institutions were selected from June 2019 to June 2021. After being interpreted by AI-based algorithms and physicians, respectively, ECG findings were classified into critical group (critical ECGs) , normal group (normal ECGs) , and positive group (abnormal but not critical ECGs) . The results interpreted by the AI-based algorithm were compared with those interpreted by physicians (defined as the gold standard) to assess the diagnostic agreement and coincidence rate between AI-based and physician-based interpretations, and to assess the diagnostic sensitivity, and positive predictive value of AI-based interpretation. And the mean time for making diagnoses of three groups of ECGs was calculated.ResultsBy the AI-based interpretation, 619, 15 634 and 45 55 ECGs were included in the critical, positive, and normal groups, respectively. And by the physician-based interpretation, 619, 15 759 and 4 430 ECGs were included in the critical, positive, and normal groups, respectively. There was high agreement between AI-based and physician-based interpretation results of ECGs〔Kappa=0.984, 95%CI (0.982, 0.987) , P<0.001〕, with a diagnostic coincidence rate of 99.4%. The diagnostic sensitivity and positive predictive value of AI-based interpretation for ECGs was 99.4%, and 100.0%, respectively. The mean time for making diagnoses of critical ECGs, abnormal but not critical ECGs, and normal ECGs was statistically different (P<0.001) , the mean time of critical critical ECGs was shorter than normal ECGs and abnormal but not critical ECGs (P<0.001) .ConclusionAI technologies used in a cloud-based platform for ECG analysis could provide physicians with support for interpreting ECGs, which may contribute to improving the interpretation accuracy, optimizing the diagnostic process, shortening the time for diagnosing a critical ECG, and the treating of critical patients in primary care. |
| first_indexed | 2024-04-24T11:59:01Z |
| format | Article |
| id | doaj.art-bea3903088f94510ad8d059c427a25d0 |
| institution | Directory Open Access Journal |
| issn | 1007-9572 |
| language | zho |
| last_indexed | 2024-04-24T11:59:01Z |
| publishDate | 2022-04-01 |
| publisher | Chinese General Practice Publishing House Co., Ltd |
| record_format | Article |
| series | Zhongguo quanke yixue |
| spelling | doaj.art-bea3903088f94510ad8d059c427a25d02024-04-09T03:10:24ZzhoChinese General Practice Publishing House Co., LtdZhongguo quanke yixue1007-95722022-04-0125111363136710.12114/j.issn.1007-9572.2021.01.411Application of Artificial Intelligence Technologies in a Cloud-based Platform for ECG Analysis to Support the Diagnosis of a Critical Electrocardiography in Primary CareYU Xinyan, GU Zhile, ZHANG Xiaojuan, ZHAO Xiaoye, ZHANG Haicheng0 1.Health Management（Physical Examination）Center，the First People's Hospital of Yinchuan，Yinchuan 750001，China 2.Damiao Community Health Station，the First People's Hospital of Yinchuan，Yinchuan 750001，China 3.School of Electrical and Information Engineering，Northern Minzu University，Yinchuan 750021，China 4.Department of Cardiology，Peking University People's Hospital，Beijing 100044，China *Corresponding author：ZHANG Haicheng，Chief physician；E-mail：haichengzhang@bjmu.edu.cn BackgroundThe cloud-based platform for electrocardiography (ECG) analysis plays a supporting role in the prevention and treatment of cardiovascular diseases. During the construction of a cloud-based platform for ECG analysis, problems that should be focused and addressed are exploring ways to better use artificial intelligence (AI) technologies supporting ECG analysis, and improving the process and effectiveness of AI-aided diagnosis of a critical ECG.ObjectiveTo explore the use of AI technologies in a cloud-based platform for ECG analysis to support the diagnosis of a critical ECG in primary care.MethodsThe 12-lead resting ECGs (n=20 808) uploaded to Nalong Cloud-based ECG Analysis Platform by primary healthcare institutions were selected from June 2019 to June 2021. After being interpreted by AI-based algorithms and physicians, respectively, ECG findings were classified into critical group (critical ECGs) , normal group (normal ECGs) , and positive group (abnormal but not critical ECGs) . The results interpreted by the AI-based algorithm were compared with those interpreted by physicians (defined as the gold standard) to assess the diagnostic agreement and coincidence rate between AI-based and physician-based interpretations, and to assess the diagnostic sensitivity, and positive predictive value of AI-based interpretation. And the mean time for making diagnoses of three groups of ECGs was calculated.ResultsBy the AI-based interpretation, 619, 15 634 and 45 55 ECGs were included in the critical, positive, and normal groups, respectively. And by the physician-based interpretation, 619, 15 759 and 4 430 ECGs were included in the critical, positive, and normal groups, respectively. There was high agreement between AI-based and physician-based interpretation results of ECGs〔Kappa=0.984, 95%CI (0.982, 0.987) , P<0.001〕, with a diagnostic coincidence rate of 99.4%. The diagnostic sensitivity and positive predictive value of AI-based interpretation for ECGs was 99.4%, and 100.0%, respectively. The mean time for making diagnoses of critical ECGs, abnormal but not critical ECGs, and normal ECGs was statistically different (P<0.001) , the mean time of critical critical ECGs was shorter than normal ECGs and abnormal but not critical ECGs (P<0.001) .ConclusionAI technologies used in a cloud-based platform for ECG analysis could provide physicians with support for interpreting ECGs, which may contribute to improving the interpretation accuracy, optimizing the diagnostic process, shortening the time for diagnosing a critical ECG, and the treating of critical patients in primary care.https://www.chinagp.net/fileup/1007-9572/PDF/1648435464222-1854732785.pdf|cardiovascular diseases|artificial intelligence|diagnostic techniques, cardiovascular|electrocardiography|remote electrocardiography cloud platform|primary medical institutions|diagnosis |
| spellingShingle | YU Xinyan, GU Zhile, ZHANG Xiaojuan, ZHAO Xiaoye, ZHANG Haicheng Application of Artificial Intelligence Technologies in a Cloud-based Platform for ECG Analysis to Support the Diagnosis of a Critical Electrocardiography in Primary Care Zhongguo quanke yixue |cardiovascular diseases|artificial intelligence|diagnostic techniques, cardiovascular|electrocardiography|remote electrocardiography cloud platform|primary medical institutions|diagnosis |
| title | Application of Artificial Intelligence
Technologies in a Cloud-based Platform for ECG Analysis to Support the
Diagnosis of a Critical Electrocardiography in Primary Care |
| title_full | Application of Artificial Intelligence
Technologies in a Cloud-based Platform for ECG Analysis to Support the
Diagnosis of a Critical Electrocardiography in Primary Care |
| title_fullStr | Application of Artificial Intelligence
Technologies in a Cloud-based Platform for ECG Analysis to Support the
Diagnosis of a Critical Electrocardiography in Primary Care |
| title_full_unstemmed | Application of Artificial Intelligence
Technologies in a Cloud-based Platform for ECG Analysis to Support the
Diagnosis of a Critical Electrocardiography in Primary Care |
| title_short | Application of Artificial Intelligence
Technologies in a Cloud-based Platform for ECG Analysis to Support the
Diagnosis of a Critical Electrocardiography in Primary Care |
| title_sort | application of artificial intelligence technologies in a cloud based platform for ecg analysis to support the diagnosis of a critical electrocardiography in primary care |
| topic | |cardiovascular diseases|artificial intelligence|diagnostic techniques, cardiovascular|electrocardiography|remote electrocardiography cloud platform|primary medical institutions|diagnosis |
| url | https://www.chinagp.net/fileup/1007-9572/PDF/1648435464222-1854732785.pdf |
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