Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography
Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 adm...
Gespeichert in:
| Veröffentlicht in: | Scientific reports 2020-11, Vol.10 (1), p.19196-19196, Article 19196 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 19196 |
|---|---|
| container_issue | 1 |
| container_start_page | 19196 |
| container_title | Scientific reports |
| container_volume | 10 |
| creator | Chen, Jun Wu, Lianlian Zhang, Jun Zhang, Liang Gong, Dexin Zhao, Yilin Chen, Qiuxiang Huang, Shulan Yang, Ming Yang, Xiao Hu, Shan Wang, Yonggui Hu, Xiao Zheng, Biqing Zhang, Kuo Wu, Huiling Dong, Zehua Xu, Youming Zhu, Yijie Chen, Xi Zhang, Mengjiao Yu, Lilei Cheng, Fan Yu, Honggang |
| description | Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Twenty-seven prospective consecutive patients in Renmin Hospital of Wuhan University were collected to evaluate the efficiency of radiologists against 2019-CoV pneumonia with that of the model. An external test was conducted in Qianjiang Central Hospital to estimate the system’s robustness. The model achieved a per-patient accuracy of 95.24% and a per-image accuracy of 98.85% in internal retrospective dataset. For 27 internal prospective patients, the system achieved a comparable performance to that of expert radiologist. In external dataset, it achieved an accuracy of 96%. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice. |
| doi_str_mv | 10.1038/s41598-020-76282-0 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7645624</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2471541097</sourcerecordid><originalsourceid>FETCH-LOGICAL-c474t-bd46eeb9656e21b36bb1178671f4011446189ce04535513786f93d891970feb73</originalsourceid><addsrcrecordid>eNp9kUtv1TAQhS0EolXbP9AFssSGjYvfjjdIqDyKVKkburacZJLrKrGDnVyp_x6XW0phgTe2dc4cz_hD6JzRC0ZF875IpmxDKKfEaN5wQl-gY06lIlxw_vLZ-QidlXJH61LcSmZfoyMhmJJK8mOUPgEseAKfY4gjaX2BHs-phwkPKeMeVujWqmBOmcUx7avQpZyi34e8FbxE2OYUg8cp4l0YdyRDSdO2hnrv0rxsaw1c05zG7Jfd_Sl6NfipwNnjfoJuv3z-fnlFrm--frv8eE06aeRK2l5qgNZqpYGzVui2Zcw02rBBUsak1KyxHdQRhVJMVGWwom8ss4YO0Bpxgj4ccpetnaHvIK7ZT27JYfb53iUf3N9KDDs3pr0zWirNZQ149xiQ048NyurmUDqYJh8hbcVxqZrKQRhVrW__sd6lLcc6XnWZ-tOM2oeO-MHV5VRKhuGpGUbdA1J3QOoqUvcLqaO16M3zMZ5KfgOsBnEwlCrFEfKft_8T-xP9o6y0</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2471541097</pqid></control><display><type>article</type><title>Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Nature Free</source><source>PubMed Central</source><source>Springer Nature OA/Free Journals</source><source>Free Full-Text Journals in Chemistry</source><creator>Chen, Jun ; Wu, Lianlian ; Zhang, Jun ; Zhang, Liang ; Gong, Dexin ; Zhao, Yilin ; Chen, Qiuxiang ; Huang, Shulan ; Yang, Ming ; Yang, Xiao ; Hu, Shan ; Wang, Yonggui ; Hu, Xiao ; Zheng, Biqing ; Zhang, Kuo ; Wu, Huiling ; Dong, Zehua ; Xu, Youming ; Zhu, Yijie ; Chen, Xi ; Zhang, Mengjiao ; Yu, Lilei ; Cheng, Fan ; Yu, Honggang</creator><creatorcontrib>Chen, Jun ; Wu, Lianlian ; Zhang, Jun ; Zhang, Liang ; Gong, Dexin ; Zhao, Yilin ; Chen, Qiuxiang ; Huang, Shulan ; Yang, Ming ; Yang, Xiao ; Hu, Shan ; Wang, Yonggui ; Hu, Xiao ; Zheng, Biqing ; Zhang, Kuo ; Wu, Huiling ; Dong, Zehua ; Xu, Youming ; Zhu, Yijie ; Chen, Xi ; Zhang, Mengjiao ; Yu, Lilei ; Cheng, Fan ; Yu, Honggang</creatorcontrib><description>Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Twenty-seven prospective consecutive patients in Renmin Hospital of Wuhan University were collected to evaluate the efficiency of radiologists against 2019-CoV pneumonia with that of the model. An external test was conducted in Qianjiang Central Hospital to estimate the system’s robustness. The model achieved a per-patient accuracy of 95.24% and a per-image accuracy of 98.85% in internal retrospective dataset. For 27 internal prospective patients, the system achieved a comparable performance to that of expert radiologist. In external dataset, it achieved an accuracy of 96%. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice.</description><identifier>ISSN: 2045-2322</identifier><identifier>EISSN: 2045-2322</identifier><identifier>DOI: 10.1038/s41598-020-76282-0</identifier><identifier>PMID: 33154542</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>631/326/2521 ; 692/699/255/2514 ; Accuracy ; Adult ; Computed tomography ; Coronaviridae ; Coronavirus Infections - complications ; Coronaviruses ; COVID-19 ; Deep Learning ; Female ; Humanities and Social Sciences ; Humans ; Image Processing, Computer-Assisted - methods ; Male ; Middle Aged ; multidisciplinary ; Pandemics ; Patients ; Pneumonia ; Pneumonia - complications ; Pneumonia - diagnostic imaging ; Pneumonia, Viral - complications ; Retrospective Studies ; Science ; Science (multidisciplinary) ; Signal-To-Noise Ratio ; Tomography, X-Ray Computed</subject><ispartof>Scientific reports, 2020-11, Vol.10 (1), p.19196-19196, Article 19196</ispartof><rights>The Author(s) 2020</rights><rights>The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c474t-bd46eeb9656e21b36bb1178671f4011446189ce04535513786f93d891970feb73</citedby><cites>FETCH-LOGICAL-c474t-bd46eeb9656e21b36bb1178671f4011446189ce04535513786f93d891970feb73</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7645624/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7645624/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,728,781,785,865,886,27926,27927,41122,42191,51578,53793,53795</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33154542$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Chen, Jun</creatorcontrib><creatorcontrib>Wu, Lianlian</creatorcontrib><creatorcontrib>Zhang, Jun</creatorcontrib><creatorcontrib>Zhang, Liang</creatorcontrib><creatorcontrib>Gong, Dexin</creatorcontrib><creatorcontrib>Zhao, Yilin</creatorcontrib><creatorcontrib>Chen, Qiuxiang</creatorcontrib><creatorcontrib>Huang, Shulan</creatorcontrib><creatorcontrib>Yang, Ming</creatorcontrib><creatorcontrib>Yang, Xiao</creatorcontrib><creatorcontrib>Hu, Shan</creatorcontrib><creatorcontrib>Wang, Yonggui</creatorcontrib><creatorcontrib>Hu, Xiao</creatorcontrib><creatorcontrib>Zheng, Biqing</creatorcontrib><creatorcontrib>Zhang, Kuo</creatorcontrib><creatorcontrib>Wu, Huiling</creatorcontrib><creatorcontrib>Dong, Zehua</creatorcontrib><creatorcontrib>Xu, Youming</creatorcontrib><creatorcontrib>Zhu, Yijie</creatorcontrib><creatorcontrib>Chen, Xi</creatorcontrib><creatorcontrib>Zhang, Mengjiao</creatorcontrib><creatorcontrib>Yu, Lilei</creatorcontrib><creatorcontrib>Cheng, Fan</creatorcontrib><creatorcontrib>Yu, Honggang</creatorcontrib><title>Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography</title><title>Scientific reports</title><addtitle>Sci Rep</addtitle><addtitle>Sci Rep</addtitle><description>Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Twenty-seven prospective consecutive patients in Renmin Hospital of Wuhan University were collected to evaluate the efficiency of radiologists against 2019-CoV pneumonia with that of the model. An external test was conducted in Qianjiang Central Hospital to estimate the system’s robustness. The model achieved a per-patient accuracy of 95.24% and a per-image accuracy of 98.85% in internal retrospective dataset. For 27 internal prospective patients, the system achieved a comparable performance to that of expert radiologist. In external dataset, it achieved an accuracy of 96%. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice.</description><subject>631/326/2521</subject><subject>692/699/255/2514</subject><subject>Accuracy</subject><subject>Adult</subject><subject>Computed tomography</subject><subject>Coronaviridae</subject><subject>Coronavirus Infections - complications</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>Deep Learning</subject><subject>Female</subject><subject>Humanities and Social Sciences</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Male</subject><subject>Middle Aged</subject><subject>multidisciplinary</subject><subject>Pandemics</subject><subject>Patients</subject><subject>Pneumonia</subject><subject>Pneumonia - complications</subject><subject>Pneumonia - diagnostic imaging</subject><subject>Pneumonia, Viral - complications</subject><subject>Retrospective Studies</subject><subject>Science</subject><subject>Science (multidisciplinary)</subject><subject>Signal-To-Noise Ratio</subject><subject>Tomography, X-Ray Computed</subject><issn>2045-2322</issn><issn>2045-2322</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kUtv1TAQhS0EolXbP9AFssSGjYvfjjdIqDyKVKkburacZJLrKrGDnVyp_x6XW0phgTe2dc4cz_hD6JzRC0ZF875IpmxDKKfEaN5wQl-gY06lIlxw_vLZ-QidlXJH61LcSmZfoyMhmJJK8mOUPgEseAKfY4gjaX2BHs-phwkPKeMeVujWqmBOmcUx7avQpZyi34e8FbxE2OYUg8cp4l0YdyRDSdO2hnrv0rxsaw1c05zG7Jfd_Sl6NfipwNnjfoJuv3z-fnlFrm--frv8eE06aeRK2l5qgNZqpYGzVui2Zcw02rBBUsak1KyxHdQRhVJMVGWwom8ss4YO0Bpxgj4ccpetnaHvIK7ZT27JYfb53iUf3N9KDDs3pr0zWirNZQ149xiQ048NyurmUDqYJh8hbcVxqZrKQRhVrW__sd6lLcc6XnWZ-tOM2oeO-MHV5VRKhuGpGUbdA1J3QOoqUvcLqaO16M3zMZ5KfgOsBnEwlCrFEfKft_8T-xP9o6y0</recordid><startdate>20201105</startdate><enddate>20201105</enddate><creator>Chen, Jun</creator><creator>Wu, Lianlian</creator><creator>Zhang, Jun</creator><creator>Zhang, Liang</creator><creator>Gong, Dexin</creator><creator>Zhao, Yilin</creator><creator>Chen, Qiuxiang</creator><creator>Huang, Shulan</creator><creator>Yang, Ming</creator><creator>Yang, Xiao</creator><creator>Hu, Shan</creator><creator>Wang, Yonggui</creator><creator>Hu, Xiao</creator><creator>Zheng, Biqing</creator><creator>Zhang, Kuo</creator><creator>Wu, Huiling</creator><creator>Dong, Zehua</creator><creator>Xu, Youming</creator><creator>Zhu, Yijie</creator><creator>Chen, Xi</creator><creator>Zhang, Mengjiao</creator><creator>Yu, Lilei</creator><creator>Cheng, Fan</creator><creator>Yu, Honggang</creator><general>Nature Publishing Group UK</general><general>Nature Publishing Group</general><scope>C6C</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>88I</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20201105</creationdate><title>Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography</title><author>Chen, Jun ; Wu, Lianlian ; Zhang, Jun ; Zhang, Liang ; Gong, Dexin ; Zhao, Yilin ; Chen, Qiuxiang ; Huang, Shulan ; Yang, Ming ; Yang, Xiao ; Hu, Shan ; Wang, Yonggui ; Hu, Xiao ; Zheng, Biqing ; Zhang, Kuo ; Wu, Huiling ; Dong, Zehua ; Xu, Youming ; Zhu, Yijie ; Chen, Xi ; Zhang, Mengjiao ; Yu, Lilei ; Cheng, Fan ; Yu, Honggang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c474t-bd46eeb9656e21b36bb1178671f4011446189ce04535513786f93d891970feb73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>631/326/2521</topic><topic>692/699/255/2514</topic><topic>Accuracy</topic><topic>Adult</topic><topic>Computed tomography</topic><topic>Coronaviridae</topic><topic>Coronavirus Infections - complications</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>Deep Learning</topic><topic>Female</topic><topic>Humanities and Social Sciences</topic><topic>Humans</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Male</topic><topic>Middle Aged</topic><topic>multidisciplinary</topic><topic>Pandemics</topic><topic>Patients</topic><topic>Pneumonia</topic><topic>Pneumonia - complications</topic><topic>Pneumonia - diagnostic imaging</topic><topic>Pneumonia, Viral - complications</topic><topic>Retrospective Studies</topic><topic>Science</topic><topic>Science (multidisciplinary)</topic><topic>Signal-To-Noise Ratio</topic><topic>Tomography, X-Ray Computed</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Jun</creatorcontrib><creatorcontrib>Wu, Lianlian</creatorcontrib><creatorcontrib>Zhang, Jun</creatorcontrib><creatorcontrib>Zhang, Liang</creatorcontrib><creatorcontrib>Gong, Dexin</creatorcontrib><creatorcontrib>Zhao, Yilin</creatorcontrib><creatorcontrib>Chen, Qiuxiang</creatorcontrib><creatorcontrib>Huang, Shulan</creatorcontrib><creatorcontrib>Yang, Ming</creatorcontrib><creatorcontrib>Yang, Xiao</creatorcontrib><creatorcontrib>Hu, Shan</creatorcontrib><creatorcontrib>Wang, Yonggui</creatorcontrib><creatorcontrib>Hu, Xiao</creatorcontrib><creatorcontrib>Zheng, Biqing</creatorcontrib><creatorcontrib>Zhang, Kuo</creatorcontrib><creatorcontrib>Wu, Huiling</creatorcontrib><creatorcontrib>Dong, Zehua</creatorcontrib><creatorcontrib>Xu, Youming</creatorcontrib><creatorcontrib>Zhu, Yijie</creatorcontrib><creatorcontrib>Chen, Xi</creatorcontrib><creatorcontrib>Zhang, Mengjiao</creatorcontrib><creatorcontrib>Yu, Lilei</creatorcontrib><creatorcontrib>Cheng, Fan</creatorcontrib><creatorcontrib>Yu, Honggang</creatorcontrib><collection>Springer Nature OA/Free Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Biology Database (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Science Database</collection><collection>Biological Science Database</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Scientific reports</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Jun</au><au>Wu, Lianlian</au><au>Zhang, Jun</au><au>Zhang, Liang</au><au>Gong, Dexin</au><au>Zhao, Yilin</au><au>Chen, Qiuxiang</au><au>Huang, Shulan</au><au>Yang, Ming</au><au>Yang, Xiao</au><au>Hu, Shan</au><au>Wang, Yonggui</au><au>Hu, Xiao</au><au>Zheng, Biqing</au><au>Zhang, Kuo</au><au>Wu, Huiling</au><au>Dong, Zehua</au><au>Xu, Youming</au><au>Zhu, Yijie</au><au>Chen, Xi</au><au>Zhang, Mengjiao</au><au>Yu, Lilei</au><au>Cheng, Fan</au><au>Yu, Honggang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography</atitle><jtitle>Scientific reports</jtitle><stitle>Sci Rep</stitle><addtitle>Sci Rep</addtitle><date>2020-11-05</date><risdate>2020</risdate><volume>10</volume><issue>1</issue><spage>19196</spage><epage>19196</epage><pages>19196-19196</pages><artnum>19196</artnum><issn>2045-2322</issn><eissn>2045-2322</eissn><abstract>Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Twenty-seven prospective consecutive patients in Renmin Hospital of Wuhan University were collected to evaluate the efficiency of radiologists against 2019-CoV pneumonia with that of the model. An external test was conducted in Qianjiang Central Hospital to estimate the system’s robustness. The model achieved a per-patient accuracy of 95.24% and a per-image accuracy of 98.85% in internal retrospective dataset. For 27 internal prospective patients, the system achieved a comparable performance to that of expert radiologist. In external dataset, it achieved an accuracy of 96%. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>33154542</pmid><doi>10.1038/s41598-020-76282-0</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2045-2322 |
| ispartof | Scientific reports, 2020-11, Vol.10 (1), p.19196-19196, Article 19196 |
| issn | 2045-2322 2045-2322 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7645624 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Nature Free; PubMed Central; Springer Nature OA/Free Journals; Free Full-Text Journals in Chemistry |
| subjects | 631/326/2521 692/699/255/2514 Accuracy Adult Computed tomography Coronaviridae Coronavirus Infections - complications Coronaviruses COVID-19 Deep Learning Female Humanities and Social Sciences Humans Image Processing, Computer-Assisted - methods Male Middle Aged multidisciplinary Pandemics Patients Pneumonia Pneumonia - complications Pneumonia - diagnostic imaging Pneumonia, Viral - complications Retrospective Studies Science Science (multidisciplinary) Signal-To-Noise Ratio Tomography, X-Ray Computed |
| title | Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-18T06%3A09%3A53IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Deep%20learning-based%20model%20for%20detecting%202019%20novel%20coronavirus%20pneumonia%20on%20high-resolution%20computed%20tomography&rft.jtitle=Scientific%20reports&rft.au=Chen,%20Jun&rft.date=2020-11-05&rft.volume=10&rft.issue=1&rft.spage=19196&rft.epage=19196&rft.pages=19196-19196&rft.artnum=19196&rft.issn=2045-2322&rft.eissn=2045-2322&rft_id=info:doi/10.1038/s41598-020-76282-0&rft_dat=%3Cproquest_pubme%3E2471541097%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2471541097&rft_id=info:pmid/33154542&rfr_iscdi=true |