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A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19).
Pan, Feng; Li, Lin; Liu, Bo; Ye, Tianhe; Li, Lingli; Liu, Dehan; Ding, Zezhen; Chen, Guangfeng; Liang, Bo; Yang, Lian; Zheng, Chuansheng.
  • Pan F; Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue #1277, Wuhan, 430022, China.
  • Li L; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China.
  • Liu B; Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue #1277, Wuhan, 430022, China.
  • Ye T; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China.
  • Li L; Shanghai Key Laboratory of Artificial Intelligence for Medical Image and Knowledge Graph, No.523 Louguanshan Road, Changning District, Shanghai, 200000, China.
  • Liu D; Hangzhou YITU Healthcare Technology Co., Ltd., Shanghai, 200000, China.
  • Ding Z; Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue #1277, Wuhan, 430022, China.
  • Chen G; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China.
  • Liang B; Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue #1277, Wuhan, 430022, China.
  • Yang L; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China.
  • Zheng C; Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue #1277, Wuhan, 430022, China.
Sci Rep ; 11(1): 417, 2021 01 11.
Article in English | MEDLINE | ID: covidwho-1019886
ABSTRACT
This study aims to explore and compare a novel deep learning-based quantification with the conventional semi-quantitative computed tomography (CT) scoring for the serial chest CT scans of COVID-19. 95 patients with confirmed COVID-19 and a total of 465 serial chest CT scans were involved, including 61 moderate patients (moderate group, 319 chest CT scans) and 34 severe patients (severe group, 146 chest CT scans). Conventional CT scoring and deep learning-based quantification were performed for all chest CT scans for two study goals (1) Correlation between these two estimations; (2) Exploring the dynamic patterns using these two estimations between moderate and severe groups. The Spearman's correlation coefficient between these two estimation methods was 0.920 (p < 0.001). predicted pulmonary involvement (CT score and percent of pulmonary lesions calculated using deep learning-based quantification) increased more rapidly and reached a higher peak on 23rd days from symptom onset in severe group, which reached a peak on 18th days in moderate group with faster absorption of the lesions. The deep learning-based quantification for COVID-19 showed a good correlation with the conventional CT scoring and demonstrated a potential benefit in the estimation of disease severities of COVID-19.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Thorax / Tomography, X-Ray Computed / Deep Learning / COVID-19 / Lung Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Adult / Aged / Female / Humans / Male / Middle aged Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-020-80261-w

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Thorax / Tomography, X-Ray Computed / Deep Learning / COVID-19 / Lung Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Adult / Aged / Female / Humans / Male / Middle aged Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-020-80261-w