Yanying Yang, Zhen Zhou, Nan Zhang, Rui Wang, Yifeng Gao, Xiaowei Ran, Zhonghua Sun, Heye Zhang, Guang Yang, Xiantao Song, Lei Xu
{"title":"基于冠状动脉计算机断层扫描血管造影的人工智能在检测冠状动脉慢性全闭塞病变中的表现。","authors":"Yanying Yang, Zhen Zhou, Nan Zhang, Rui Wang, Yifeng Gao, Xiaowei Ran, Zhonghua Sun, Heye Zhang, Guang Yang, Xiantao Song, Lei Xu","doi":"10.21037/cdt-23-407","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Coronary chronic total occlusion (CTO) increases the risk of developing major adverse cardiovascular events (MACE) and cardiogenic shock. Coronary computed tomography angiography (CCTA) is a safe, noninvasive method to diagnose CTO lesions. With the development of artificial intelligence (AI), AI has been broadly applied in cardiovascular images, but AI-based detection of CTO lesions from CCTA images is difficult. We aim to evaluate the performance of AI in detecting the CTO lesions of coronary arteries based on CCTA images.</p><p><strong>Methods: </strong>We retrospectively and consecutively enrolled patients with 50% stenosis, 50-99% stenosis, and CTO lesions who received CCTA scans between June 2021 and June 2022 in Beijing Anzhen Hospital. Four-fifths of them were randomly assigned to the training dataset, while the rest (1/5) were randomly assigned to the testing dataset. Performance of the AI-assisted CCTA (CCTA-AI) in detecting the CTO lesions was evaluated through sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and receiver operating characteristic analysis. With invasive coronary angiography as the reference, the diagnostic performance of AI method and manual method was compared.</p><p><strong>Results: </strong>A total of 537 patients with 1,569 stenotic lesions (including 672 lesions with <50% stenosis, 493 lesions with 50-99% stenosis, and 404 CTO lesions) were enrolled in our study. CCTA-AI saved 75% of the time in post-processing and interpreting the CCTA images when compared to the manual method (116±15 <i>vs.</i> 472±45 seconds). In the testing dataset, the accuracy of CCTA-AI in detecting CTO lesions was 86.2% (79.0%, 90.3%), with the area under the curve of 0.874. No significant difference was found in detecting CTO lesions between AI and manual methods (P=0.53).</p><p><strong>Conclusions: </strong>AI can automatically detect CTO lesions based on CCTA images, with high diagnostic accuracy and efficiency.</p>","PeriodicalId":9592,"journal":{"name":"Cardiovascular diagnosis and therapy","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11384454/pdf/","citationCount":"0","resultStr":"{\"title\":\"Performance of artificial intelligence in detecting the chronic total occlusive lesions of coronary artery based on coronary computed tomographic angiography.\",\"authors\":\"Yanying Yang, Zhen Zhou, Nan Zhang, Rui Wang, Yifeng Gao, Xiaowei Ran, Zhonghua Sun, Heye Zhang, Guang Yang, Xiantao Song, Lei Xu\",\"doi\":\"10.21037/cdt-23-407\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Coronary chronic total occlusion (CTO) increases the risk of developing major adverse cardiovascular events (MACE) and cardiogenic shock. Coronary computed tomography angiography (CCTA) is a safe, noninvasive method to diagnose CTO lesions. With the development of artificial intelligence (AI), AI has been broadly applied in cardiovascular images, but AI-based detection of CTO lesions from CCTA images is difficult. We aim to evaluate the performance of AI in detecting the CTO lesions of coronary arteries based on CCTA images.</p><p><strong>Methods: </strong>We retrospectively and consecutively enrolled patients with 50% stenosis, 50-99% stenosis, and CTO lesions who received CCTA scans between June 2021 and June 2022 in Beijing Anzhen Hospital. Four-fifths of them were randomly assigned to the training dataset, while the rest (1/5) were randomly assigned to the testing dataset. Performance of the AI-assisted CCTA (CCTA-AI) in detecting the CTO lesions was evaluated through sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and receiver operating characteristic analysis. With invasive coronary angiography as the reference, the diagnostic performance of AI method and manual method was compared.</p><p><strong>Results: </strong>A total of 537 patients with 1,569 stenotic lesions (including 672 lesions with <50% stenosis, 493 lesions with 50-99% stenosis, and 404 CTO lesions) were enrolled in our study. CCTA-AI saved 75% of the time in post-processing and interpreting the CCTA images when compared to the manual method (116±15 <i>vs.</i> 472±45 seconds). In the testing dataset, the accuracy of CCTA-AI in detecting CTO lesions was 86.2% (79.0%, 90.3%), with the area under the curve of 0.874. No significant difference was found in detecting CTO lesions between AI and manual methods (P=0.53).</p><p><strong>Conclusions: </strong>AI can automatically detect CTO lesions based on CCTA images, with high diagnostic accuracy and efficiency.</p>\",\"PeriodicalId\":9592,\"journal\":{\"name\":\"Cardiovascular diagnosis and therapy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-08-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11384454/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cardiovascular diagnosis and therapy\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.21037/cdt-23-407\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/6/20 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"CARDIAC & CARDIOVASCULAR SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cardiovascular diagnosis and therapy","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.21037/cdt-23-407","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/6/20 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
Performance of artificial intelligence in detecting the chronic total occlusive lesions of coronary artery based on coronary computed tomographic angiography.
Background: Coronary chronic total occlusion (CTO) increases the risk of developing major adverse cardiovascular events (MACE) and cardiogenic shock. Coronary computed tomography angiography (CCTA) is a safe, noninvasive method to diagnose CTO lesions. With the development of artificial intelligence (AI), AI has been broadly applied in cardiovascular images, but AI-based detection of CTO lesions from CCTA images is difficult. We aim to evaluate the performance of AI in detecting the CTO lesions of coronary arteries based on CCTA images.
Methods: We retrospectively and consecutively enrolled patients with 50% stenosis, 50-99% stenosis, and CTO lesions who received CCTA scans between June 2021 and June 2022 in Beijing Anzhen Hospital. Four-fifths of them were randomly assigned to the training dataset, while the rest (1/5) were randomly assigned to the testing dataset. Performance of the AI-assisted CCTA (CCTA-AI) in detecting the CTO lesions was evaluated through sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and receiver operating characteristic analysis. With invasive coronary angiography as the reference, the diagnostic performance of AI method and manual method was compared.
Results: A total of 537 patients with 1,569 stenotic lesions (including 672 lesions with <50% stenosis, 493 lesions with 50-99% stenosis, and 404 CTO lesions) were enrolled in our study. CCTA-AI saved 75% of the time in post-processing and interpreting the CCTA images when compared to the manual method (116±15 vs. 472±45 seconds). In the testing dataset, the accuracy of CCTA-AI in detecting CTO lesions was 86.2% (79.0%, 90.3%), with the area under the curve of 0.874. No significant difference was found in detecting CTO lesions between AI and manual methods (P=0.53).
Conclusions: AI can automatically detect CTO lesions based on CCTA images, with high diagnostic accuracy and efficiency.
期刊介绍：
The journal ''Cardiovascular Diagnosis and Therapy'' (Print ISSN: 2223-3652; Online ISSN: 2223-3660) accepts basic and clinical science submissions related to Cardiovascular Medicine and Surgery. The mission of the journal is the rapid exchange of scientific information between clinicians and scientists worldwide. To reach this goal, the journal will focus on novel media, using a web-based, digital format in addition to traditional print-version. This includes on-line submission, review, publication, and distribution. The digital format will also allow submission of extensive supporting visual material, both images and video. The website www.thecdt.org will serve as the central hub and also allow posting of comments and on-line discussion. The web-site of the journal will be linked to a number of international web-sites (e.g. www.dxy.cn), which will significantly expand the distribution of its contents.
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