Progress in the Clinical Application of Artificial Intelligence for Left Ventricle Analysis in Cardiac Magnetic Resonance
Cardiac magnetic resonance (CMR) imaging enables a one-stop assessment of heart structure and function. Artificial intelligence (AI) can simplify and automate work flows and improve image post-processing speed and diagnostic accuracy; thus, it greatly affects many aspects of CMR. This review highlig...
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| Format: | Article |
| Language: | English |
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IMR Press
2024-12-01
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| Series: | Reviews in Cardiovascular Medicine |
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| Online Access: | https://www.imrpress.com/journal/RCM/25/12/10.31083/j.rcm2512447 |
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| author | Yinghui Le Chongshang Zhao Jing An Jiali Zhou Dongdong Deng Yi He |
| author_facet | Yinghui Le Chongshang Zhao Jing An Jiali Zhou Dongdong Deng Yi He |
| author_sort | Yinghui Le |
| collection | DOAJ |
| description | Cardiac magnetic resonance (CMR) imaging enables a one-stop assessment of heart structure and function. Artificial intelligence (AI) can simplify and automate work flows and improve image post-processing speed and diagnostic accuracy; thus, it greatly affects many aspects of CMR. This review highlights the application of AI for left heart analysis in CMR, including quality control, image segmentation, and global and regional functional assessment. Most recent research has focused on segmentation of the left ventricular myocardium and blood pool. Although many algorithms have shown a level comparable to that of human experts, some problems, such as poor performance of basal and apical segmentation and false identification of myocardial structure, remain. Segmentation of myocardial fibrosis is another research hotspot, and most patient cohorts of such studies have hypertrophic cardiomyopathy. Whether the above methods are applicable to other patient groups requires further study. The use of automated CMR interpretation for the diagnosis and prognosis assessment of cardiovascular diseases demonstrates great clinical potential. However, prospective large-scale clinical trials are needed to investigate the real-word application of AI technology in clinical practice. |
| format | Article |
| id | doaj-art-bdbf61e3c95142c09719b9f02b2dd40f |
| institution | Kabale University |
| issn | 1530-6550 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | IMR Press |
| record_format | Article |
| series | Reviews in Cardiovascular Medicine |
| spelling | doaj-art-bdbf61e3c95142c09719b9f02b2dd40f2024-12-30T09:51:26ZengIMR PressReviews in Cardiovascular Medicine1530-65502024-12-01251244710.31083/j.rcm2512447S1530-6550(24)01568-0Progress in the Clinical Application of Artificial Intelligence for Left Ventricle Analysis in Cardiac Magnetic ResonanceYinghui Le0Chongshang Zhao1Jing An2Jiali Zhou3Dongdong Deng4Yi He5Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 100050 Beijing, ChinaKey Laboratory for Biomedical Engineering of Ministry of Education, Institute of Biomedical Engineering, Zhejiang University, 310058 Hangzhou, Zhejiang, ChinaSiemens Shenzhen Magnetic Resonance, MR Collaboration NE Asia, 518000 Shenzhen, Guangdong, ChinaDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, 100050 Beijing, ChinaSchool of Biomedical Engineering, Dalian University of Technology, 116024 Dalian, Liaoning, ChinaDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, 100050 Beijing, ChinaCardiac magnetic resonance (CMR) imaging enables a one-stop assessment of heart structure and function. Artificial intelligence (AI) can simplify and automate work flows and improve image post-processing speed and diagnostic accuracy; thus, it greatly affects many aspects of CMR. This review highlights the application of AI for left heart analysis in CMR, including quality control, image segmentation, and global and regional functional assessment. Most recent research has focused on segmentation of the left ventricular myocardium and blood pool. Although many algorithms have shown a level comparable to that of human experts, some problems, such as poor performance of basal and apical segmentation and false identification of myocardial structure, remain. Segmentation of myocardial fibrosis is another research hotspot, and most patient cohorts of such studies have hypertrophic cardiomyopathy. Whether the above methods are applicable to other patient groups requires further study. The use of automated CMR interpretation for the diagnosis and prognosis assessment of cardiovascular diseases demonstrates great clinical potential. However, prospective large-scale clinical trials are needed to investigate the real-word application of AI technology in clinical practice.https://www.imrpress.com/journal/RCM/25/12/10.31083/j.rcm2512447cardiovascular magnetic resonanceartificial intelligenceleft ventricle |
| spellingShingle | Yinghui Le Chongshang Zhao Jing An Jiali Zhou Dongdong Deng Yi He Progress in the Clinical Application of Artificial Intelligence for Left Ventricle Analysis in Cardiac Magnetic Resonance Reviews in Cardiovascular Medicine cardiovascular magnetic resonance artificial intelligence left ventricle |
| title | Progress in the Clinical Application of Artificial Intelligence for Left Ventricle Analysis in Cardiac Magnetic Resonance |
| title_full | Progress in the Clinical Application of Artificial Intelligence for Left Ventricle Analysis in Cardiac Magnetic Resonance |
| title_fullStr | Progress in the Clinical Application of Artificial Intelligence for Left Ventricle Analysis in Cardiac Magnetic Resonance |
| title_full_unstemmed | Progress in the Clinical Application of Artificial Intelligence for Left Ventricle Analysis in Cardiac Magnetic Resonance |
| title_short | Progress in the Clinical Application of Artificial Intelligence for Left Ventricle Analysis in Cardiac Magnetic Resonance |
| title_sort | progress in the clinical application of artificial intelligence for left ventricle analysis in cardiac magnetic resonance |
| topic | cardiovascular magnetic resonance artificial intelligence left ventricle |
| url | https://www.imrpress.com/journal/RCM/25/12/10.31083/j.rcm2512447 |
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