Quantitative evaluation of COVID-19 pneumonia severity by CT pneumonia analysis algorithm using deep learning technology and blood test results-Reference-Cited by-同舟云学术

Quantitative evaluation of COVID-19 pneumonia severity by CT pneumonia analysis algorithm using deep learning technology and blood test results

Published:2021-05-14 Issue:10 Volume:39 Page:956-965
ISSN:1867-1071
Container-title:Japanese Journal of Radiology
language:en
Short-container-title:Jpn J Radiol

Author:

Okuma Tomohisa,Hamamoto Shinichi,Maebayashi Tetsunori,Taniguchi Akishige,Hirakawa Kyoko,Matsushita Shu,Matsushita Kazuki,Murata Katsuko,Manabe Takao,Miki Yukio

Publisher

Springer Science and Business Media LLC

Subject

Radiology Nuclear Medicine and imaging

Link

https://link.springer.com/content/pdf/10.1007/s11604-021-01134-4.pdf

Reference27 articles.

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2. Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, et al. Prediction models for diagnosis and prognosis of COVID-19 infection: systematic review and critical appraisal. BMJ. 2020; 369:m1328. https://doi.org/10.1136/bmj.m1328.

3. World Health Organization. Clinical management of COVID-19. Interim guidance. 27 May 2020. https://www.who.int/publications-detail/clinical-management-of-covid-19. Accessed 28 May 2020.

4. Himoto Y, Sakata A, Kirita M, Hiroi T, Kobayashi K, Kubo K, et al. Diagnostic performance of chest CT to differentiate COVID-19 pneumonia in non-high-epidemic area in Japan. Jpn J Radiol. 2020;38:400–6.

5. Cellina M, Orsi M, Valenti Pittino C, Toluian T, Oliva G. Chest computed tomography findings of COVID-19 pneumonia: pictorial essay with literature review. Jpn J Radiol. 2020;38:1012–9.

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