Advances in Artificial Intelligence-Assisted Coronary Computed Tomographic Angiography for Atherosclerotic Plaque Characterization
Coronary artery disease is a leading cause of death worldwide. Major adverse cardiac events are associated not only with coronary luminal stenosis but also with atherosclerotic plaque components. Coronary computed tomography angiography (CCTA) enables non-invasive evaluation of atherosclerotic plaqu...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IMR Press
2024-01-01
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| Series: | Reviews in Cardiovascular Medicine |
| Subjects: | |
| Online Access: | https://www.imrpress.com/journal/RCM/25/1/10.31083/j.rcm2501027 |
| _version_ | 1827368454860046336 |
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| author | Qian Chen Fan Zhou Guanghui Xie Chun Xiang Tang Xiaofei Gao Yamei Zhang Xindao Yin Hui Xu Long Jiang Zhang |
| author_facet | Qian Chen Fan Zhou Guanghui Xie Chun Xiang Tang Xiaofei Gao Yamei Zhang Xindao Yin Hui Xu Long Jiang Zhang |
| author_sort | Qian Chen |
| collection | DOAJ |
| description | Coronary artery disease is a leading cause of death worldwide. Major adverse cardiac events are associated not only with coronary luminal stenosis but also with atherosclerotic plaque components. Coronary computed tomography angiography (CCTA) enables non-invasive evaluation of atherosclerotic plaque along the entire coronary tree. However, precise and efficient assessment of plaque features on CCTA is still a challenge for physicians in daily practice. Artificial intelligence (AI) refers to algorithms that can simulate intelligent human behavior to improve clinical work efficiency. Recently, cardiovascular imaging has seen remarkable advancements with the use of AI. AI-assisted CCTA has the potential to facilitate the clinical workflow, offer objective and repeatable quantitative results, accelerate the interpretation of reports, and guide subsequent treatment. Several AI algorithms have been developed to provide a comprehensive assessment of atherosclerotic plaques. This review serves to highlight the cutting-edge applications of AI-assisted CCTA in atherosclerosis plaque characterization, including detecting obstructive plaques, assessing plaque volumes and vulnerability, monitoring plaque progression, and providing risk assessment. Finally, this paper discusses the current problems and future directions for implementing AI in real-world clinical settings. |
| first_indexed | 2024-03-08T09:31:40Z |
| format | Article |
| id | doaj.art-2b610d32c06845ccbef83d647893639e |
| institution | Directory Open Access Journal |
| issn | 1530-6550 |
| language | English |
| last_indexed | 2024-03-08T09:31:40Z |
| publishDate | 2024-01-01 |
| publisher | IMR Press |
| record_format | Article |
| series | Reviews in Cardiovascular Medicine |
| spelling | doaj.art-2b610d32c06845ccbef83d647893639e2024-01-31T01:12:56ZengIMR PressReviews in Cardiovascular Medicine1530-65502024-01-012512710.31083/j.rcm2501027S1530-6550(23)01143-2Advances in Artificial Intelligence-Assisted Coronary Computed Tomographic Angiography for Atherosclerotic Plaque CharacterizationQian Chen0Fan Zhou1Guanghui Xie2Chun Xiang Tang3Xiaofei Gao4Yamei Zhang5Xindao Yin6Hui Xu7Long Jiang Zhang8Department of Radiology, Nanjing First Hospital, Nanjing Medical University, 210006 Nanjing, Jiangsu, ChinaDepartment of Radiology, Jinling Hospital, Medical School of Nanjing University, 210002 Nanjing, Jiangsu, ChinaDepartment of Radiology, Nanjing First Hospital, Nanjing Medical University, 210006 Nanjing, Jiangsu, ChinaDepartment of Radiology, Jinling Hospital, Medical School of Nanjing University, 210002 Nanjing, Jiangsu, ChinaDepartment of Cardiology, Nanjing First Hospital, Nanjing Medical University, 210006 Nanjing, Jiangsu, ChinaDepartment of Radiology, Nanjing First Hospital, Nanjing Medical University, 210006 Nanjing, Jiangsu, ChinaDepartment of Radiology, Nanjing First Hospital, Nanjing Medical University, 210006 Nanjing, Jiangsu, ChinaDepartment of Radiology, Nanjing First Hospital, Nanjing Medical University, 210006 Nanjing, Jiangsu, ChinaDepartment of Radiology, Jinling Hospital, Medical School of Nanjing University, 210002 Nanjing, Jiangsu, ChinaCoronary artery disease is a leading cause of death worldwide. Major adverse cardiac events are associated not only with coronary luminal stenosis but also with atherosclerotic plaque components. Coronary computed tomography angiography (CCTA) enables non-invasive evaluation of atherosclerotic plaque along the entire coronary tree. However, precise and efficient assessment of plaque features on CCTA is still a challenge for physicians in daily practice. Artificial intelligence (AI) refers to algorithms that can simulate intelligent human behavior to improve clinical work efficiency. Recently, cardiovascular imaging has seen remarkable advancements with the use of AI. AI-assisted CCTA has the potential to facilitate the clinical workflow, offer objective and repeatable quantitative results, accelerate the interpretation of reports, and guide subsequent treatment. Several AI algorithms have been developed to provide a comprehensive assessment of atherosclerotic plaques. This review serves to highlight the cutting-edge applications of AI-assisted CCTA in atherosclerosis plaque characterization, including detecting obstructive plaques, assessing plaque volumes and vulnerability, monitoring plaque progression, and providing risk assessment. Finally, this paper discusses the current problems and future directions for implementing AI in real-world clinical settings.https://www.imrpress.com/journal/RCM/25/1/10.31083/j.rcm2501027artificial intelligencecoronary ct angiographycoronary plaquedeep learning |
| spellingShingle | Qian Chen Fan Zhou Guanghui Xie Chun Xiang Tang Xiaofei Gao Yamei Zhang Xindao Yin Hui Xu Long Jiang Zhang Advances in Artificial Intelligence-Assisted Coronary Computed Tomographic Angiography for Atherosclerotic Plaque Characterization Reviews in Cardiovascular Medicine artificial intelligence coronary ct angiography coronary plaque deep learning |
| title | Advances in Artificial Intelligence-Assisted Coronary Computed Tomographic Angiography for Atherosclerotic Plaque Characterization |
| title_full | Advances in Artificial Intelligence-Assisted Coronary Computed Tomographic Angiography for Atherosclerotic Plaque Characterization |
| title_fullStr | Advances in Artificial Intelligence-Assisted Coronary Computed Tomographic Angiography for Atherosclerotic Plaque Characterization |
| title_full_unstemmed | Advances in Artificial Intelligence-Assisted Coronary Computed Tomographic Angiography for Atherosclerotic Plaque Characterization |
| title_short | Advances in Artificial Intelligence-Assisted Coronary Computed Tomographic Angiography for Atherosclerotic Plaque Characterization |
| title_sort | advances in artificial intelligence assisted coronary computed tomographic angiography for atherosclerotic plaque characterization |
| topic | artificial intelligence coronary ct angiography coronary plaque deep learning |
| url | https://www.imrpress.com/journal/RCM/25/1/10.31083/j.rcm2501027 |
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