{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:36:11Z","timestamp":1792787771050,"version":"4.5.0"},"reference-count":28,"publisher":"European Respiratory Society (ERS)","issue":"2","license":[{"start":{"date-parts":[[2020,5,22]],"date-time":"2020-05-22T00:00:00Z","timestamp":1590105600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2017YFA0205200"],"award-info":[{"award-number":["2017YFA0205200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Novel Coronavirus Pneumonia Emergency Key Project of Science and Technology of Hubei Province","award":["2020FCA015"],"award-info":[{"award-number":["2020FCA015"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81227901"],"award-info":[{"award-number":["81227901"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81871332"],"award-info":[{"award-number":["81871332"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81930053"],"award-info":[{"award-number":["81930053"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["publications.ersnet.org"],"crossmark-restriction":true},"short-container-title":["Eur Respir J"],"accepted":{"date-parts":[[2020,5,16]]},"published-print":{"date-parts":[[2020,8]]},"abstract":"<jats:p>Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19 and finding high-risk patients with worse prognosis for early prevention and medical resource optimisation is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography.<\/jats:p>\n                  <jats:p>We retrospectively collected 5372 patients with computed tomography images from seven cities or provinces. Firstly, 4106 patients with computed tomography images were used to pre-train the deep learning system, making it learn lung features. Following this, 1266 patients (924 with COVID-19 (471 had follow-up for &gt;5\u2005days) and 342 with other pneumonia) from six cities or provinces were enrolled to train and externally validate the performance of the deep learning system.<\/jats:p>\n                  <jats:p>In the four external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC 0.87 and 0.88, respectively) and viral pneumonia (AUC 0.86). Moreover, the deep learning system succeeded to stratify patients into high- and low-risk groups whose hospital-stay time had significant difference (p=0.013 and p=0.014, respectively). Without human assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings.<\/jats:p>\n                  <jats:p>Deep learning provides a convenient tool for fast screening of COVID-19 and identifying potential high-risk patients, which may be helpful for medical resource optimisation and early prevention before patients show severe symptoms.<\/jats:p>","DOI":"10.1183\/13993003.00775-2020","type":"journal-article","created":{"date-parts":[[2020,5,22]],"date-time":"2020-05-22T14:29:50Z","timestamp":1590157790000},"page":"2000775","update-policy":"https:\/\/doi.org\/10.1183\/ers-crossmark-policy","source":"Crossref","is-referenced-by-count":400,"title":["A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis"],"prefix":"10.1183","volume":"56","author":[{"given":"Shuo","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunfei","family":"Zha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weimin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingxia","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Niu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meiyun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoming","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongjun","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"He","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Gong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Bai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongbei","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liusu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"81","published-online":{"date-parts":[[2020,5,22]]},"reference":[{"key":"2024102101431914000_56.2.2000775.1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2020.1585"},{"key":"2024102101431914000_56.2.2000775.2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa2001316"},{"key":"2024102101431914000_56.2.2000775.3","doi-asserted-by":"publisher","DOI":"10.1016\/S2213-2600(20)30079-5"},{"key":"2024102101431914000_56.2.2000775.4","doi-asserted-by":"crossref","first-page":"e480","DOI":"10.1016\/S2214-109X(20)30068-1","article-title":"Potential association between COVID-19 mortality and health-care resource availability","volume":"8","author":"Ji","year":"2020","journal-title":"Lancet Glob Health"},{"key":"2024102101431914000_56.2.2000775.5","doi-asserted-by":"crossref","first-page":"E41","DOI":"10.1148\/radiol.2020200343","article-title":"Chest CT for typical 2019-nCoV pneumonia: relationship to negative RT-PCR testing","volume":"296","author":"Xie","year":"2020","journal-title":"Radiology"},{"key":"2024102101431914000_56.2.2000775.6","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200642"},{"key":"2024102101431914000_56.2.2000775.7","doi-asserted-by":"publisher","DOI":"10.1183\/13993003.00334-2020"},{"key":"2024102101431914000_56.2.2000775.8","doi-asserted-by":"publisher","DOI":"10.1183\/13993003.00398-2020"},{"key":"2024102101431914000_56.2.2000775.9","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/S1473-3099(20)30086-4","article-title":"Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study","volume":"20","author":"Shi","year":"2020","journal-title":"Lancet Infect Dis"},{"key":"2024102101431914000_56.2.2000775.10","doi-asserted-by":"publisher","DOI":"10.1016\/S1473-3099(20)30134-1"},{"key":"2024102101431914000_56.2.2000775.11","doi-asserted-by":"publisher","DOI":"10.1183\/13993003.00986-2018"},{"key":"2024102101431914000_56.2.2000775.12","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1016\/S2213-2600(18)30286-8","article-title":"Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: a case-cohort study","volume":"6","author":"Walsh","year":"2018","journal-title":"Lancet Respir Med"},{"key":"2024102101431914000_56.2.2000775.13","doi-asserted-by":"crossref","unstructured":"Walsh SL , Humphries SM , Wells AU , et al. Imaging research in fibrotic lung disease; applying deep learning to unsolved problems. Lancet Respir Med 2020; in press [https:\/\/doi.org\/10.1016\/S2213-2600(20)30003-5].","DOI":"10.1016\/S2213-2600(20)30003-5"},{"key":"2024102101431914000_56.2.2000775.14","doi-asserted-by":"publisher","DOI":"10.1183\/13993003.01216-2019"},{"key":"2024102101431914000_56.2.2000775.15","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.media.2017.06.014","article-title":"Central focused convolutional neural networks: developing a data-driven model for lung nodule segmentation","volume":"40","author":"Wang","year":"2017","journal-title":"Med Image Anal"},{"key":"2024102101431914000_56.2.2000775.16","doi-asserted-by":"crossref","unstructured":"Wang S , Kang B , Ma J , et al. A deep learning algorithm using CT images to screen for corona virus disease (COVID-19). medRxiv 2020; preprint [https:\/\/doi.org\/10.1101\/2020.02.14.20023028].","DOI":"10.1101\/2020.02.14.20023028"},{"key":"2024102101431914000_56.2.2000775.17","doi-asserted-by":"crossref","unstructured":"Xu X , Jiang X , Ma C , et al. Deep learning system to screen coronavirus disease 2019 pneumonia. arXiv 2020; preprint [https:\/\/arxiv.org\/abs\/2002.09334].","DOI":"10.1016\/j.eng.2020.04.010"},{"key":"2024102101431914000_56.2.2000775.18","doi-asserted-by":"crossref","unstructured":"Huang G , Liu Z , Van Der Maaten L , et al. Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017; pp. 4700\u20134708.","DOI":"10.1109\/CVPR.2017.243"},{"key":"2024102101431914000_56.2.2000775.19","doi-asserted-by":"crossref","unstructured":"Lin T-Y , Doll\u00e1r P , Girshick R , et al. Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017; pp. 2117\u20132125.","DOI":"10.1109\/CVPR.2017.106"},{"key":"2024102101431914000_56.2.2000775.20","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1016\/j.media.2014.07.003","article-title":"Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study","volume":"18","author":"Rudyanto","year":"2014","journal-title":"Med Image Anal"},{"key":"2024102101431914000_56.2.2000775.21","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.radonc.2018.10.019","article-title":"Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer","volume":"132","author":"Wang","year":"2019","journal-title":"Radiother Oncol"},{"key":"2024102101431914000_56.2.2000775.22","doi-asserted-by":"crossref","unstructured":"Selvaraju RR , Cogswell M , Das A , et al. Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision. 2017; pp. 618\u2013626.","DOI":"10.1109\/ICCV.2017.74"},{"key":"2024102101431914000_56.2.2000775.23","unstructured":"Kotikalapudi R . 2017. Keras Visulization Toolkit. https:\/\/github.com\/raghakot\/keras-vis."},{"key":"2024102101431914000_56.2.2000775.24","doi-asserted-by":"crossref","unstructured":"Jin S , Wang B , Xu H , et al. AI-assisted CT imaging analysis for COVID-19 screening: building and deploying a medical AI system in four weeks. medRxiv 2020; preprint [https:\/\/doi.org\/10.1101\/2020.03.19.20039354]","DOI":"10.1016\/j.asoc.2020.106897"},{"key":"2024102101431914000_56.2.2000775.25","doi-asserted-by":"crossref","unstructured":"Song Y , Zheng S , Li L , et al. Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. medRxiv 2020; preprint [https:\/\/doi.org\/10.1101\/2020.02.23.20026930].","DOI":"10.1109\/TCBB.2021.3065361"},{"key":"2024102101431914000_56.2.2000775.26","doi-asserted-by":"crossref","unstructured":"Jin C , Chen W , Cao Y , et al. Development and evaluation of an AI system for COVID-19 diagnosis. medRxiv 2020; preprint [https:\/\/doi.org\/10.1101\/2020.03.20.20039834].","DOI":"10.1038\/s41467-020-18685-1"},{"key":"2024102101431914000_56.2.2000775.27","doi-asserted-by":"crossref","unstructured":"Li L , Qin L , Xu Z , et al. Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology 2020: in press [https:\/\/dx.doi.org\/10.1148\/radiol.2020200905].","DOI":"10.1148\/radiol.2020200905"},{"key":"2024102101431914000_56.2.2000775.28","doi-asserted-by":"crossref","unstructured":"Zheng C , Deng X , Fu Q , et al. Deep learning-based detection for COVID-19 from chest CT using weak label. medRxiv 2020; preprint [https:\/\/doi.org\/10.1101\/2020.03.12.20027185].","DOI":"10.1101\/2020.03.12.20027185"}],"container-title":["European Respiratory Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1183\/13993003.00775-2020","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T02:34:09Z","timestamp":1740364449000},"score":1,"resource":{"primary":{"URL":"https:\/\/publications.ersnet.org\/lookup\/doi\/10.1183\/13993003.00775-2020"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,22]]},"references-count":28,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,8,6]]},"published-print":{"date-parts":[[2020,8]]}},"alternative-id":["10.1183\/13993003.00775-2020"],"URL":"https:\/\/doi.org\/10.1183\/13993003.00775-2020","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2020.03.24.20042317","asserted-by":"object"}]},"ISSN":["0903-1936","1399-3003"],"issn-type":[{"value":"0903-1936","type":"print"},{"value":"1399-3003","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,22]]}}}