{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T17:27:28Z","timestamp":1793381248534,"version":"4.5.0"},"reference-count":79,"publisher":"Oxford University Press (OUP)","issue":"1113","license":[{"start":{"date-parts":[[2020,2,6]],"date-time":"2020-02-06T00:00:00Z","timestamp":1580947200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,9,1]]},"abstract":"<jats:p>In this review, we describe the technical aspects of artificial intelligence (AI) in cardiac imaging, starting with radiomics, basic algorithms of deep learning and application tasks of algorithms, until recently the availability of the public database. Subsequently, we conducted a systematic literature search for recently published clinically relevant studies on AI in cardiac imaging. As a result, 24 and 14 studies using CT and MRI, respectively, were included and summarized. From these studies, it can be concluded that AI is widely applied in cardiac applications in the clinic, including coronary calcium scoring, coronary CT angiography, fractional flow reserve CT, plaque analysis, left ventricular myocardium analysis, diagnosis of myocardial infarction, prognosis of coronary artery disease, assessment of cardiac function, and diagnosis and prognosis of cardiomyopathy. These advancements show that AI has a promising prospect in cardiac imaging.<\/jats:p>","DOI":"10.1259\/bjr.20190812","type":"journal-article","created":{"date-parts":[[2020,2,4]],"date-time":"2020-02-04T18:48:23Z","timestamp":1580842103000},"source":"Crossref","is-referenced-by-count":88,"title":["Development and application of artificial intelligence in cardiac imaging"],"prefix":"10.1093","volume":"93","author":[{"given":"Beibei","family":"Jiang","sequence":"first","affiliation":[{"name":"Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100 , Shanghai 200080 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Guo","sequence":"additional","affiliation":[{"name":"Shukun (Beijing) Technology Co, Ltd., Jinhui Bd, Qiyang Rd , Beijing 100102 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinghui","family":"Ge","sequence":"additional","affiliation":[{"name":"Radiology 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Available from:\nhttps:\/\/ui.adsabs.harvard.edu\/abs\/2014arXiv1412.3555C\n."},{"key":"2024050215192919500_b13","doi-asserted-by":"crossref","unstructured":"Bai \u00a0W\n\u00a0Suzuki \u00a0H\n\u00a0Qin \u00a0C\n\u00a0Tarroni \u00a0G\n\u00a0Oktay \u00a0O\n\u00a0Matthews \u00a0PM\n.\nRecurrent neural networks for aortic image sequence segmentation with sparse annotations. arXiv e-prints [serial on the Internet]\n.\n2018\n. 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Available from:\nhttps:\/\/ui.adsabs.harvard.edu\/abs\/2019arXiv190712246C\n."},{"key":"2024050215192919500_b22","doi-asserted-by":"crossref","first-page":"1835","DOI":"10.1109\/TMI.2018.2804799","article-title":"Deep learning for quantification of epicardial and thoracic adipose tissue from non-contrast CT","volume":"37","author":"Commandeur","year":"2018","journal-title":"IEEE Trans Med Imaging"},{"key":"2024050215192919500_b23","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1109\/TMI.2017.2769839","article-title":"Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions","volume":"37","author":"Lessmann","year":"2018","journal-title":"IEEE Trans Med Imaging"},{"key":"2024050215192919500_b24","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.media.2016.04.004","article-title":"Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks","volume":"34","author":"Wolterink","year":"2016","journal-title":"Med Image Anal"},{"key":"2024050215192919500_b25","unstructured":"Tran \u00a0PV\n          \n.\nA fully convolutional neural network for cardiac segmentation in short-axis MRI. arXiv e-prints [serial on the Internet]\n.\n2016\n. 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Available from:\nhttps:\/\/ui.adsabs.harvard.edu\/abs\/2018arXiv180200121Z\n.","DOI":"10.1109\/CVPR.2019.00642"},{"key":"2024050215192919500_b77","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1016\/j.mcna.2015.02.011","article-title":"Cardiac MRI: a general overview with emphasis on current use and indications","volume":"99","author":"Pfeiffer","year":"2015","journal-title":"Med Clin North Am"},{"key":"2024050215192919500_b78","unstructured":"Goodfellow \u00a0IJ\n\u00a0Pouget-Abadie \u00a0J\n\u00a0Mirza \u00a0M\n\u00a0Xu \u00a0B\n\u00a0Warde-Farley \u00a0D\n\u00a0Ozair \u00a0S\n.\nGenerative adversarial networks. arXiv e-prints [serial on the Internet]\n.\n2014\n. 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