Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review
Abstract Background Artificial intelligence (AI) technology has been increasingly developed and studied in cardiac imaging. This systematic review summarizes the latest progress of image segmentation, quantification, and the clinical application of AI in evaluating cardiac adipose tissue. Methods We...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2021-07-01
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| Series: | European Journal of Hybrid Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41824-021-00107-0 |
| _version_ | 1818923785353428992 |
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| author | Lu Zhang Jianqing Sun Beibei Jiang Lingyun Wang Yaping Zhang Xueqian Xie |
| author_facet | Lu Zhang Jianqing Sun Beibei Jiang Lingyun Wang Yaping Zhang Xueqian Xie |
| author_sort | Lu Zhang |
| collection | DOAJ |
| description | Abstract Background Artificial intelligence (AI) technology has been increasingly developed and studied in cardiac imaging. This systematic review summarizes the latest progress of image segmentation, quantification, and the clinical application of AI in evaluating cardiac adipose tissue. Methods We exhaustively searched PubMed and the Web of Science for publications prior to 30 April 2021. The search included eligible studies that used AI for image analysis of epicardial adipose tissue (EAT) or pericoronary adipose tissue (PCAT). The risk of bias and concerns regarding applicability were assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Results Of the 140 initially identified citation records, 19 high-quality studies were eligible for this systematic review, including 15 (79%) on the image segmentation and quantification of EAT or PCAT and 4 (21%) on the clinical application of EAT or PCAT in cardiovascular diseases. All 19 included studies were rated as low risk of bias in terms of flow and timing, reference standards, and the index test and as having low concern of applicability in terms of reference standards and patient selection, but 16 (84%) studies did not conduct external validation. Conclusion AI technology can provide accurate and quicker methods to segment and quantify EAT and PCAT images and shows potential value in the diagnosis and risk prediction of cardiovascular diseases. AI is expected to expand the value of cardiac adipose tissue imaging. |
| first_indexed | 2024-12-20T02:14:57Z |
| format | Article |
| id | doaj.art-4dbd7c49d5bc4f3f88e0542d790c96e3 |
| institution | Directory Open Access Journal |
| issn | 2510-3636 |
| language | English |
| last_indexed | 2024-12-20T02:14:57Z |
| publishDate | 2021-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | European Journal of Hybrid Imaging |
| spelling | doaj.art-4dbd7c49d5bc4f3f88e0542d790c96e32022-12-21T19:56:58ZengSpringerOpenEuropean Journal of Hybrid Imaging2510-36362021-07-015111310.1186/s41824-021-00107-0Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic reviewLu Zhang0Jianqing Sun1Beibei Jiang2Lingyun Wang3Yaping Zhang4Xueqian Xie5Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of MedicineShukun (Beijing) Technology Co., Ltd.Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of MedicineRadiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of MedicineRadiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of MedicineRadiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of MedicineAbstract Background Artificial intelligence (AI) technology has been increasingly developed and studied in cardiac imaging. This systematic review summarizes the latest progress of image segmentation, quantification, and the clinical application of AI in evaluating cardiac adipose tissue. Methods We exhaustively searched PubMed and the Web of Science for publications prior to 30 April 2021. The search included eligible studies that used AI for image analysis of epicardial adipose tissue (EAT) or pericoronary adipose tissue (PCAT). The risk of bias and concerns regarding applicability were assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Results Of the 140 initially identified citation records, 19 high-quality studies were eligible for this systematic review, including 15 (79%) on the image segmentation and quantification of EAT or PCAT and 4 (21%) on the clinical application of EAT or PCAT in cardiovascular diseases. All 19 included studies were rated as low risk of bias in terms of flow and timing, reference standards, and the index test and as having low concern of applicability in terms of reference standards and patient selection, but 16 (84%) studies did not conduct external validation. Conclusion AI technology can provide accurate and quicker methods to segment and quantify EAT and PCAT images and shows potential value in the diagnosis and risk prediction of cardiovascular diseases. AI is expected to expand the value of cardiac adipose tissue imaging.https://doi.org/10.1186/s41824-021-00107-0Artificial intelligenceEpicardial adipose tissuePericoronary adipose tissueMachine learningDeep learningRadiomics |
| spellingShingle | Lu Zhang Jianqing Sun Beibei Jiang Lingyun Wang Yaping Zhang Xueqian Xie Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review European Journal of Hybrid Imaging Artificial intelligence Epicardial adipose tissue Pericoronary adipose tissue Machine learning Deep learning Radiomics |
| title | Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review |
| title_full | Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review |
| title_fullStr | Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review |
| title_full_unstemmed | Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review |
| title_short | Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review |
| title_sort | development of artificial intelligence in epicardial and pericoronary adipose tissue imaging a systematic review |
| topic | Artificial intelligence Epicardial adipose tissue Pericoronary adipose tissue Machine learning Deep learning Radiomics |
| url | https://doi.org/10.1186/s41824-021-00107-0 |
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