Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software.
Background: The novel coronavirus disease 2019 (COVID-19) is an emerging worldwide threat to public health. While chest computed tomography (CT) plays an indispensable role in its diagnosis, the quantification and localization of lesions cannot be accurately assessed manually. We employed deep learn...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 47; no. 11; pp. 2525 - 2533 |
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| Autores principales: | , , , , , , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Oct2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=146054948&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146054948 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Oct2020 vid: 47 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 146054948 144558465 10.1007/s00259-020-04953-1 146054948 ppf: 2525 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software. aug: au: Zhang, Hai-tao Zhang, Jin-song Zhang, Hai-hua Nan, Yan-dong Zhao, Ying Fu, En-qing Xie, Yong-hong Liu, Wei Li, Wang-ping Zhang, Hong-jun Jiang, Hua Li, Chun-mei Li, Yan-yan Ma, Rui-na Dang, Shao-kang Gao, Bo-bo Zhang, Xi-jing Zhang, Tao affil: Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, 710038, Xi'an, China sug: ab: Background: The novel coronavirus disease 2019 (COVID-19) is an emerging worldwide threat to public health. While chest computed tomography (CT) plays an indispensable role in its diagnosis, the quantification and localization of lesions cannot be accurately assessed manually. We employed deep learning-based software to aid in detection, localization and quantification of COVID-19 pneumonia. Methods: A total of 2460 RT-PCR tested SARS-CoV-2-positive patients (1250 men and 1210 women; mean age, 57.7 ± 14.0 years (age range, 11–93 years) were retrospectively identified from Huoshenshan Hospital in Wuhan from February 11 to March 16, 2020. Basic clinical characteristics were reviewed. The uAI Intelligent Assistant Analysis System was used to assess the CT scans. Results: CT scans of 2215 patients (90%) showed multiple lesions of which 36 (1%) and 50 patients (2%) had left and right lung infections, respectively (> 50% of each affected lung's volume), while 27 (1%) had total lung infection (> 50% of the total volume of both lungs). Overall, 298 (12%), 778 (32%) and 1300 (53%) patients exhibited pure ground glass opacities (GGOs), GGOs with sub-solid lesions and GGOs with both sub-solid and solid lesions, respectively. Moreover, 2305 (94%) and 71 (3%) patients presented primarily with GGOs and sub-solid lesions, respectively. Elderly patients (≥ 60 years) were more likely to exhibit sub-solid lesions. The generalized linear mixed model showed that the dorsal segment of the right lower lobe was the favoured site of COVID-19 pneumonia. Conclusion: Chest CT combined with analysis by the uAI Intelligent Assistant Analysis System can accurately evaluate pneumonia in COVID-19 patients. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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