{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T23:43:42Z","timestamp":1793835822044,"version":"4.6.1"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T00:00:00Z","timestamp":1704758400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T00:00:00Z","timestamp":1704758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["81971612"],"award-info":[{"award-number":["81971612"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Shanghai General Hospital Academic Leaders Training Program","id":[{"id":"https:\/\/ror.org\/04a46mh28","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Shanghai General Hospital Clinical Research Project","award":["CCTR-2022ZD01"],"award-info":[{"award-number":["CCTR-2022ZD01"]}],"id":[{"id":"https:\/\/ror.org\/04a46mh28","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"DOI":"10.1007\/s00330-023-10494-6","type":"journal-article","created":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T02:02:43Z","timestamp":1704765763000},"page":"4909-4919","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Fully automated artificial intelligence-based coronary CT angiography image processing: efficiency, diagnostic capability, and risk stratification"],"prefix":"10.1007","volume":"34","author":[{"given":"Yaping","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianqing","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenhong","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingyun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keke","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijie","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingyao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6669-0097","authenticated-orcid":false,"given":"Xueqian","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,9]]},"reference":[{"key":"10494_CR1","doi-asserted-by":"publisher","first-page":"2982","DOI":"10.1016\/j.jacc.2020.11.010","volume":"76","author":"GA Roth","year":"2020","unstructured":"Roth GA, Mensah GA, Johnson CO et al (2020) Global burden of cardiovascular diseases and risk factors, 1990\u20132019: update from the GBD 2019 study. J Am Coll Cardiol 76:2982\u20133021","journal-title":"J Am Coll Cardiol"},{"key":"10494_CR2","doi-asserted-by":"publisher","first-page":"l1945","DOI":"10.1136\/bmj.l1945","volume":"365","author":"R Haase","year":"2019","unstructured":"Haase R, Schlattmann P, Gueret P et al (2019) Diagnosis of obstructive coronary artery disease using computed tomography angiography in patients with stable chest pain depending on clinical probability and in clinically important subgroups: meta-analysis of individual patient data. BMJ 365:l1945","journal-title":"BMJ"},{"key":"10494_CR3","first-page":"e368","volume":"144","author":"M Gulati","year":"2021","unstructured":"Gulati M, Levy PD, Mukherjee D et al (2021) 2021 AHA\/ACC\/ASE\/CHEST\/SAEM\/SCCT\/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: executive summary: a report of the American College of Cardiology\/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation 144:e368\u2013e454","journal-title":"Circulation"},{"key":"10494_CR4","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1016\/j.jcct.2014.07.003","volume":"8","author":"J Leipsic","year":"2014","unstructured":"Leipsic J, Abbara S, Achenbach S et al (2014) SCCT guidelines for the interpretation and reporting of coronary CT angiography: a report of the Society of Cardiovascular Computed Tomography Guidelines Committee. J Cardiovasc Comput Tomogr 8:342\u2013358","journal-title":"J Cardiovasc Comput Tomogr"},{"key":"10494_CR5","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.jacr.2018.07.022","volume":"16","author":"DC Levin","year":"2019","unstructured":"Levin DC, Parker L, Halpern EJ, Rao VM (2019) Coronary CT angiography: reversal of earlier utilization trends. J Am Coll Radiol 16:147\u2013155","journal-title":"J Am Coll Radiol"},{"key":"10494_CR6","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1148\/radiol.210551","volume":"303","author":"B Jiang","year":"2022","unstructured":"Jiang B, Li N, Shi X et al (2022) Deep learning reconstruction shows better lung nodule detection for ultra-low-dose chest CT. Radiology 303:202\u2013212","journal-title":"Radiology"},{"key":"10494_CR7","doi-asserted-by":"publisher","first-page":"e2255113","DOI":"10.1001\/jamanetworkopen.2022.55113","volume":"6","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Liu M, Zhang L et al (2023) Comparison of chest radiograph captions based on natural language processing vs completed by radiologists. JAMA Netw Open 6:e2255113","journal-title":"JAMA Netw Open"},{"key":"10494_CR8","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1038\/s43856-021-00043-x","volume":"1","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Liu M, Hu S et al (2021) Development and multicenter validation of chest X-ray radiography interpretations based on natural language processing. Commun Med (Lond) 1:43","journal-title":"Commun Med (Lond)"},{"key":"10494_CR9","doi-asserted-by":"publisher","first-page":"732","DOI":"10.1093\/eurheartj\/ehaa1065","volume":"42","author":"C Antoniades","year":"2021","unstructured":"Antoniades C, Asselbergs FW, Vardas P (2021) The year in cardiovascular medicine 2020: digital health and innovation. Eur Heart J 42:732\u2013739","journal-title":"Eur Heart J"},{"key":"10494_CR10","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1007\/s00330-019-06447-7","volume":"30","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, van der Werf NR, Jiang B, van Hamersvelt R, Greuter MJW, Xie X (2020) Motion-corrected coronary calcium scores by a convolutional neural network: a robotic simulating study. Eur Radiol 30:1285\u20131294","journal-title":"Eur Radiol"},{"key":"10494_CR11","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1186\/s41824-021-00107-0","volume":"5","author":"L Zhang","year":"2021","unstructured":"Zhang L, Sun J, Jiang B, Wang L, Zhang Y, Xie X (2021) Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review. Eur J Hybrid Imaging 5:14","journal-title":"Eur J Hybrid Imaging"},{"key":"10494_CR12","doi-asserted-by":"publisher","first-page":"20190812","DOI":"10.1259\/bjr.20190812","volume":"93","author":"B Jiang","year":"2020","unstructured":"Jiang B, Guo N, Ge Y, Zhang L, Oudkerk M, Xie X (2020) Development and application of artificial intelligence in cardiac imaging. Br J Radiol 93:20190812","journal-title":"Br J Radiol"},{"key":"10494_CR13","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.media.2018.10.005","volume":"51","author":"JM Wolterink","year":"2019","unstructured":"Wolterink JM, van Hamersvelt RW, Viergever MA, Leiner T, Isgum I (2019) Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier. Med Image Anal 51:46\u201360","journal-title":"Med Image Anal"},{"key":"10494_CR14","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1007\/s11548-018-1884-6","volume":"14","author":"D Wu","year":"2019","unstructured":"Wu D, Wang X, Bai J et al (2019) Automated anatomical labeling of coronary arteries via bidirectional tree LSTMs. Int J Comput Assist Radiol Surg 14:271\u2013280","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"10494_CR15","doi-asserted-by":"publisher","first-page":"101721","DOI":"10.1016\/j.compmedimag.2020.101721","volume":"83","author":"S Candemir","year":"2020","unstructured":"Candemir S, White RD, Demirer M et al (2020) Automated coronary artery atherosclerosis detection and weakly supervised localization on coronary CT angiography with a deep 3-dimensional convolutional neural network. Comput Med Imaging Graph 83:101721","journal-title":"Comput Med Imaging Graph"},{"key":"10494_CR16","doi-asserted-by":"publisher","first-page":"105651","DOI":"10.1016\/j.cmpb.2020.105651","volume":"196","author":"D Han","year":"2020","unstructured":"Han D, Liu J, Sun Z, Cui Y, He Y, Yang Z (2020) Deep learning analysis in coronary computed tomographic angiography imaging for the assessment of patients with coronary artery stenosis. Comput Methods Programs Biomed 196:105651","journal-title":"Comput Methods Programs Biomed"},{"key":"10494_CR17","doi-asserted-by":"publisher","first-page":"109835","DOI":"10.1016\/j.ejrad.2021.109835","volume":"142","author":"CY Liu","year":"2021","unstructured":"Liu CY, Tang CX, Zhang XL et al (2021) Deep learning powered coronary CT angiography for detecting obstructive coronary artery disease: The effect of reader experience, calcification and image quality. Eur J Radiol 142:109835","journal-title":"Eur J Radiol"},{"key":"10494_CR18","doi-asserted-by":"publisher","first-page":"707508","DOI":"10.3389\/fcvm.2021.707508","volume":"8","author":"L Xu","year":"2021","unstructured":"Xu L, He Y, Luo N et al (2021) Diagnostic accuracy and generalizability of a deep learning-based fully automated algorithm for coronary artery stenosis detection on CCTA: a multi-centre registry study. Front Cardiovasc Med 8:707508","journal-title":"Front Cardiovasc Med"},{"key":"10494_CR19","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1093\/eurheartj\/ehaa575","volume":"42","author":"JP Collet","year":"2021","unstructured":"Collet JP, Thiele H, Barbato E et al (2021) 2020 ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation. Eur Heart J 42:1289\u20131367","journal-title":"Eur Heart J"},{"key":"10494_CR20","doi-asserted-by":"publisher","first-page":"2342","DOI":"10.1002\/mp.15521","volume":"49","author":"PM Adamson","year":"2022","unstructured":"Adamson PM, Bhattbhatt V, Principi S et al (2022) Technical note: Evaluation of a V-Net autosegmentation algorithm for pediatric CT scans: performance, generalizability, and application to patient-specific CT dosimetry. Med Phys 49:2342\u20132354","journal-title":"Med Phys"},{"key":"10494_CR21","doi-asserted-by":"publisher","first-page":"1099","DOI":"10.1016\/j.jcmg.2016.05.005","volume":"9","author":"RC Cury","year":"2016","unstructured":"Cury RC, Abbara S, Achenbach S et al (2016) Coronary artery disease - reporting and data system (CAD-RADS): an expert consensus document of SCCT, ACR and NASCI: endorsed by the ACC. JACC Cardiovasc Imaging 9:1099\u20131113","journal-title":"JACC Cardiovasc Imaging"},{"key":"10494_CR22","doi-asserted-by":"publisher","first-page":"987","DOI":"10.1109\/TNNLS.2021.3104860","volume":"34","author":"H Ran","year":"2023","unstructured":"Ran H, Wen S, Li Q, Cao Y, Shi K, Huang T (2023) Compact and stable memristive visual geometry group neural network. IEEE Trans Neural Netw Learn Syst 34:987\u2013998","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10494_CR23","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1001\/jama.2022.0358","volume":"327","author":"DL Bhatt","year":"2022","unstructured":"Bhatt DL, Lopes RD, Harrington RA (2022) Diagnosis and treatment of acute coronary syndromes: a review. JAMA 327:662\u2013675","journal-title":"JAMA"},{"key":"10494_CR24","doi-asserted-by":"publisher","first-page":"1591","DOI":"10.1056\/NEJMoa2200963","volume":"386","author":"P Maurovich-Horvat","year":"2022","unstructured":"Maurovich-Horvat P, Bosserdt M, Kofoed KF et al (2022) CT or invasive coronary angiography in stable chest pain. N Engl J Med 386:1591\u20131602","journal-title":"N Engl J Med"},{"key":"10494_CR25","doi-asserted-by":"publisher","first-page":"692329","DOI":"10.3389\/fonc.2021.692329","volume":"11","author":"G Liu","year":"2021","unstructured":"Liu G, Xu Z, Zhang Y et al (2021) Machine-learning-derived nomogram based on 3D radiomic features and clinical factors predicts progression-free survival in lung adenocarcinoma. Front Oncol 11:692329","journal-title":"Front Oncol"},{"key":"10494_CR26","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.atherosclerosis.2019.12.001","volume":"294","author":"G Muscogiuri","year":"2020","unstructured":"Muscogiuri G, Chiesa M, Trotta M et al (2020) Performance of a deep learning algorithm for the evaluation of CAD-RADS classification with CCTA. Atherosclerosis 294:25\u201332","journal-title":"Atherosclerosis"},{"key":"10494_CR27","doi-asserted-by":"publisher","first-page":"1588","DOI":"10.1109\/TMI.2018.2883807","volume":"38","author":"M Zreik","year":"2019","unstructured":"Zreik M, van Hamersvelt RW, Wolterink JM, Leiner T, Viergever MA, Isgum I (2019) A recurrent CNN for automatic detection and classification of coronary artery plaque and stenosis in coronary CT angiography. IEEE Trans Med Imaging 38:1588\u20131598","journal-title":"IEEE Trans Med Imaging"},{"key":"10494_CR28","doi-asserted-by":"publisher","DOI":"10.2174\/1573405619666221221092933","author":"A Laidi","year":"2022","unstructured":"Laidi A, Ammar M, Daho MEH, Mahmoudi S (2022) Deep learning models for coronary atherosclerosis detection in coronary CT angiography. Curr Med Imaging. https:\/\/doi.org\/10.2174\/1573405619666221221092933","journal-title":"Curr Med Imaging"},{"key":"10494_CR29","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.jcct.2015.11.004","volume":"10","author":"K Liu","year":"2016","unstructured":"Liu K, Hsieh C, Zhuang N et al (2016) Current utilization of cardiac computed tomography in mainland China: a national survey. J Cardiovasc Comput Tomogr 10:76\u201381","journal-title":"J Cardiovasc Comput Tomogr"},{"key":"10494_CR30","doi-asserted-by":"publisher","first-page":"106484","DOI":"10.1016\/j.compbiomed.2022.106484","volume":"153","author":"M Penso","year":"2023","unstructured":"Penso M, Moccia S, Caiani EG et al (2023) A token-mixer architecture for CAD-RADS classification of coronary stenosis on multiplanar reconstruction CT images. Comput Biol Med 153:106484","journal-title":"Comput Biol Med"},{"key":"10494_CR31","doi-asserted-by":"crossref","unstructured":"Huang Z, Ding Y, Yang Y et al (2023) Performance of machine learning-based coronary computed tomography angiography for selecting revascularization candidates. Acta Radiol 2841851231158730","DOI":"10.1177\/02841851231158730"},{"key":"10494_CR32","doi-asserted-by":"publisher","first-page":"2949","DOI":"10.1093\/eurheartj\/eht296","volume":"34","author":"G Montalescot","year":"2013","unstructured":"Montalescot G, Sechtem U, Achenbach S et al (2013) 2013 ESC guidelines on the management of stable coronary artery disease: the Task Force on the management of stable coronary artery disease of the European Society of Cardiology. Eur Heart J 34:2949\u20133003","journal-title":"Eur Heart J"},{"key":"10494_CR33","doi-asserted-by":"publisher","first-page":"1458","DOI":"10.1016\/j.jacr.2016.04.024","volume":"13","author":"RC Cury","year":"2016","unstructured":"Cury RC, Abbara S, Achenbach S et al (2016) CAD-RADS: Coronary Artery Disease - Reporting and Data System: an expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Radiology (ACR) and the North American Society for Cardiovascular Imaging (NASCI). J Am Coll Radiol 13:1458-1466 e9","journal-title":"J Am Coll Radiol"},{"key":"10494_CR34","doi-asserted-by":"publisher","first-page":"2511","DOI":"10.1016\/j.jacc.2018.02.079","volume":"71","author":"HJ Chang","year":"2018","unstructured":"Chang HJ, Lin FY, Lee SE et al (2018) Coronary atherosclerotic precursors of acute coronary syndromes. J Am Coll Cardiol 71:2511\u20132522","journal-title":"J Am Coll Cardiol"},{"key":"10494_CR35","doi-asserted-by":"publisher","first-page":"922","DOI":"10.1007\/s11547-023-01658-x","volume":"128","author":"T Polidori","year":"2023","unstructured":"Polidori T, De Santis D, Rucci C et al (2023) Radiomics applications in cardiac imaging: a comprehensive review. Radiol Med 128:922\u2013933","journal-title":"Radiol Med"},{"key":"10494_CR36","doi-asserted-by":"publisher","first-page":"e221693","DOI":"10.1148\/radiol.221693","volume":"307","author":"Q Chen","year":"2023","unstructured":"Chen Q, Pan T, Wang YN et al (2023) A coronary CT angiography radiomics model to identify vulnerable plaque and predict cardiovascular events. Radiology 307:e221693","journal-title":"Radiology"},{"key":"10494_CR37","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-023-09809-4","author":"C Feng","year":"2023","unstructured":"Feng C, Chen R, Dong S et al (2023) Predicting coronary plaque progression with conventional plaque parameters and radiomics features derived from coronary CT angiography. Eur Radiol. https:\/\/doi.org\/10.1007\/s00330-023-09809-4","journal-title":"Eur Radiol"},{"key":"10494_CR38","doi-asserted-by":"publisher","first-page":"980996","DOI":"10.3389\/fphys.2022.980996","volume":"13","author":"Y Feng","year":"2022","unstructured":"Feng Y, Xu Z, Zhang L et al (2022) Machine-learning-derived radiomics signature of pericoronary tissue in coronary CT angiography associates with functional ischemia. Front Physiol 13:980996","journal-title":"Front Physiol"}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-023-10494-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-023-10494-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-023-10494-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T03:05:31Z","timestamp":1721185531000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00330-023-10494-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,9]]},"references-count":38,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["10494"],"URL":"https:\/\/doi.org\/10.1007\/s00330-023-10494-6","relation":{},"ISSN":["1432-1084"],"issn-type":[{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,9]]},"assertion":[{"value":"30 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 September 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 October 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The scientific guarantor of this publication is Prof. Xueqian Xie.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Guarantor"}},{"value":"The authors declare no competing financial interests but the following competing non-financial interests. Jianqing Sun, Zhenhong Ding, and Ning Guo are employees of Shukun (Beijing) Technology Co, Ltd., who participated in the development of the system and provided advice on the technical part of the manuscript but did not participate in validating the system and analyzing the data.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No complex statistical methods were necessary for this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was not required for this study because a commercial National Medical Products Administration (NMPA)-approved software system was used.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional Review Board approval was obtained.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"No study subjects or cohorts have been previously reported.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"value":"\u2022 Prospective\u2022 Observational\u2022 Multicenter study","order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}