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Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software.
Eur J Nucl Med Mol Imaging. 2020 10; 47(11):2525-2532.EJ

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.

Authors+Show Affiliations

Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, China. Wuhan Huoshenshan Hospital, Wuhan, 430100, China.Wuhan Huoshenshan Hospital, Wuhan, 430100, China. Department of Radiology, Xijing Hospital, Air Force Military Medical University, Xi'an, 710038, China.Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, China.Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, China. Wuhan Huoshenshan Hospital, Wuhan, 430100, China.Wuhan Huoshenshan Hospital, Wuhan, 430100, China. Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, China.Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, China. Wuhan Huoshenshan Hospital, Wuhan, 430100, China.Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, China. Wuhan Huoshenshan Hospital, Wuhan, 430100, China.Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, China. Wuhan Huoshenshan Hospital, Wuhan, 430100, China.Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, China. Wuhan Huoshenshan Hospital, Wuhan, 430100, China.Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, China. Wuhan Huoshenshan Hospital, Wuhan, 430100, China.Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, China. Wuhan Huoshenshan Hospital, Wuhan, 430100, China.Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, China. Wuhan Huoshenshan Hospital, Wuhan, 430100, China.Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, China. Wuhan Huoshenshan Hospital, Wuhan, 430100, China.Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, China. Wuhan Huoshenshan Hospital, Wuhan, 430100, China.Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, China. Wuhan Huoshenshan Hospital, Wuhan, 430100, China.Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, China. Wuhan Huoshenshan Hospital, Wuhan, 430100, China.Wuhan Huoshenshan Hospital, Wuhan, 430100, China. zhangxj918@163.com. Department of Critical Care Medicine, Xijing Hospital, Air Force Military Medical University, Xi'an, 710038, China. zhangxj918@163.com.Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, China. zhangft@fmmu.edu.cn. Wuhan Huoshenshan Hospital, Wuhan, 430100, China. zhangft@fmmu.edu.cn.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

32666395

Citation

Zhang, Hai-Tao, et al. "Automated Detection and Quantification of COVID-19 Pneumonia: CT Imaging Analysis By a Deep Learning-based Software." European Journal of Nuclear Medicine and Molecular Imaging, vol. 47, no. 11, 2020, pp. 2525-2532.
Zhang HT, Zhang JS, Zhang HH, et al. Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software. Eur J Nucl Med Mol Imaging. 2020;47(11):2525-2532.
Zhang, H. T., Zhang, J. S., Zhang, H. H., Nan, Y. D., Zhao, Y., Fu, E. Q., Xie, Y. H., Liu, W., Li, W. P., Zhang, H. J., Jiang, H., Li, C. M., Li, Y. Y., Ma, R. N., Dang, S. K., Gao, B. B., Zhang, X. J., & Zhang, T. (2020). Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software. European Journal of Nuclear Medicine and Molecular Imaging, 47(11), 2525-2532. https://doi.org/10.1007/s00259-020-04953-1
Zhang HT, et al. Automated Detection and Quantification of COVID-19 Pneumonia: CT Imaging Analysis By a Deep Learning-based Software. Eur J Nucl Med Mol Imaging. 2020;47(11):2525-2532. PubMed PMID: 32666395.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software. AU - Zhang,Hai-Tao, AU - Zhang,Jin-Song, AU - Zhang,Hai-Hua, AU - Nan,Yan-Dong, AU - Zhao,Ying, AU - Fu,En-Qing, AU - Xie,Yong-Hong, AU - Liu,Wei, AU - Li,Wang-Ping, AU - Zhang,Hong-Jun, AU - Jiang,Hua, AU - Li,Chun-Mei, AU - Li,Yan-Yan, AU - Ma,Rui-Na, AU - Dang,Shao-Kang, AU - Gao,Bo-Bo, AU - Zhang,Xi-Jing, AU - Zhang,Tao, Y1 - 2020/07/14/ PY - 2020/03/30/received PY - 2020/07/05/accepted PY - 2020/7/16/pubmed PY - 2020/10/3/medline PY - 2020/7/16/entrez KW - 2019 novel coronavirus KW - Artificial intelligence (AI) KW - Computed tomography (CT) KW - Ground glass opacity (GGO) KW - Viral pneumonia SP - 2525 EP - 2532 JF - European journal of nuclear medicine and molecular imaging JO - Eur J Nucl Med Mol Imaging VL - 47 IS - 11 N2 - 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. SN - 1619-7089 UR - https://www.unboundmedicine.com/medline/citation/32666395/Automated_detection_and_quantification_of_COVID_19_pneumonia:_CT_imaging_analysis_by_a_deep_learning_based_software_ L2 - https://dx.doi.org/10.1007/s00259-020-04953-1 DB - PRIME DP - Unbound Medicine ER -