Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software
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ORIGINAL ARTICLE
Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software Hai-tao Zhang 1,2 & Jin-song Zhang 2,3 & Hai-hua Zhang 1 & Yan-dong Nan 1,2 & Ying Zhao 2,4 & En-qing Fu 1,2 & Yong-hong Xie 1,2 & Wei Liu 1,2 & Wang-ping Li 1,2 & Hong-jun Zhang 1,2 & Hua Jiang 1,2 & Chun-mei Li 1,2 & Yan-yan Li 1,2 & Rui-na Ma 1,2 & Shao-kang Dang 1,2 & Bo-bo Gao 1,2 & Xi-jing Zhang 2,5 & Tao Zhang 1,2 Received: 30 March 2020 / Accepted: 5 July 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract 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. Keywords 2019 novel coronavirus . Viral pneumonia . Artificial intelligence (AI) . Computed tomography (CT) . Ground glass opacity (GGO)
Hai-tao Zhang, Jin-song Zhang and Hai-hua Zhang contributed equally to this work. This article is part of the Topical Collection on Advanced Image Analyses (Radiomics and Artificial Intelligence) * Xi-jing Zhang [email protected] * Tao Zhang [email protected] 1
Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi’an 710038, China
2
Wuhan Huoshenshan Hospital, Wuhan 430100, China
3
Department of Radiology, Xijing Hospital, Air Force Military Medical University, Xi’an 710038, China
4
Tangdu Hospital, Air Force Military Medical University, Xi’an 710038, China
5
Department of Critica
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