Novel Deep Learning Technique Used in Management and Discharge of Hospitalized Patients with COVID-19 in China

Qingcheng Meng,1 Wentao Liu,1 Pengrui Gao,1 Jiaqi Zhang,2 Anlan Sun,2 Jia Ding,2 Hao Liu,2 Ziqiao Lei3 1Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, People’s Republic of China; 2Yizhun Medical AI Co. Ltd, Beijing, People’s Republic o...

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Autores principales:	Meng Q, Liu W, Gao P, Zhang J, Sun A, Ding J, Liu H, Lei Z
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2020
Materias:	x-ray computed tomography sars-cov-2 infectious disease lung disease Therapeutics. Pharmacology RM1-950
Acceso en línea:	https://doaj.org/article/87cfb874fb9849bdaadb535f5092d3e5
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spelling	oai:doaj.org-article:87cfb874fb9849bdaadb535f5092d3e52021-12-02T11:41:52ZNovel Deep Learning Technique Used in Management and Discharge of Hospitalized Patients with COVID-19 in China1178-203Xhttps://doaj.org/article/87cfb874fb9849bdaadb535f5092d3e52020-12-01T00:00:00Zhttps://www.dovepress.com/novel-deep-learning-technique-used-in-management-and-discharge-of-hosp-peer-reviewed-article-TCRMhttps://doaj.org/toc/1178-203XQingcheng Meng,1 Wentao Liu,1 Pengrui Gao,1 Jiaqi Zhang,2 Anlan Sun,2 Jia Ding,2 Hao Liu,2 Ziqiao Lei3 1Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, People’s Republic of China; 2Yizhun Medical AI Co. Ltd, Beijing, People’s Republic of China; 3Department of Radiology, The Wuhan Union Hospital, Wuhan, People’s Republic of ChinaCorrespondence: Qingcheng MengDepartment of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, No. 127 Dongming Road, Jinshui District, Zhengzhou 450008, People’s Republic of ChinaTel/Fax +86-0371-65587152Email zlyymengqingcheng1865@zzu.edu.cnZiqiao LeiDepartment of Radiology, The Wuhan Union Hospital, No. 1277 Jiefang Road, Jianghan District, Wuhan 430000, People’s Republic of ChinaEmail ziqiao_lei@hust.edu.cnPurpose: The low sensitivity and false-negative results of nucleic acid testing greatly affect its performance in diagnosing and discharging patients with coronavirus disease (COVID-19). Chest computed tomography (CT)-based evaluation of pneumonia may indicate a need for isolation. Therefore, this radiologic modality plays an important role in managing patients with suspected COVID-19. Meanwhile, deep learning (DL) technology has been successful in detecting various imaging features of chest CT. This study applied a novel DL technique to standardize the discharge criteria of COVID-19 patients with consecutive negative respiratory pathogen nucleic acid test results at a “square cabin” hospital.Patients and Methods: DL was used to evaluate the chest CT scans of 270 hospitalized COVID-19 patients who had two consecutive negative nucleic acid tests (sampling interval > 1 day). The CT scans evaluated were obtained after the patients’ second negative test result. The standard criterion determined by DL for patient discharge was a total volume ratio of lesion to lung < 50%.Results: The mean number of days between hospitalization and DL was 14.3 (± 2.4). The average intersection over union was 0.7894. Two hundred and thirteen (78.9%) patients exhibited pneumonia, of whom 54.0% (115/213) had mild interstitial fibrosis. Twenty-one, 33, and 4 cases exhibited vascular enlargement, pleural thickening, and mediastinal lymphadenopathy, respectively. Of the latter, 18.8% (40/213) had a total volume ratio of lesions to lung ≥ 50% according to our severity scale and were monitored continuously in the hospital. Three cases had a positive follow-up nucleic acid test during hospitalization. None of the 230 discharged cases later tested positive or exhibited pneumonia progression.Conclusion: The novel DL enables the accurate management of hospitalized patients with COVID-19 and can help avoid cluster transmission or exacerbation in patients with false-negative acid test.Keywords: X-ray, computed tomography, SARS-CoV-2, infectious disease, lung diseaseMeng QLiu WGao PZhang JSun ADing JLiu HLei ZDove Medical Pressarticlex-raycomputed tomographysars-cov-2infectious diseaselung diseaseTherapeutics. PharmacologyRM1-950ENTherapeutics and Clinical Risk Management, Vol Volume 16, Pp 1195-1201 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	x-ray computed tomography sars-cov-2 infectious disease lung disease Therapeutics. Pharmacology RM1-950
spellingShingle	x-ray computed tomography sars-cov-2 infectious disease lung disease Therapeutics. Pharmacology RM1-950 Meng Q Liu W Gao P Zhang J Sun A Ding J Liu H Lei Z Novel Deep Learning Technique Used in Management and Discharge of Hospitalized Patients with COVID-19 in China
description	Qingcheng Meng,1 Wentao Liu,1 Pengrui Gao,1 Jiaqi Zhang,2 Anlan Sun,2 Jia Ding,2 Hao Liu,2 Ziqiao Lei3 1Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, People’s Republic of China; 2Yizhun Medical AI Co. Ltd, Beijing, People’s Republic of China; 3Department of Radiology, The Wuhan Union Hospital, Wuhan, People’s Republic of ChinaCorrespondence: Qingcheng MengDepartment of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, No. 127 Dongming Road, Jinshui District, Zhengzhou 450008, People’s Republic of ChinaTel/Fax +86-0371-65587152Email zlyymengqingcheng1865@zzu.edu.cnZiqiao LeiDepartment of Radiology, The Wuhan Union Hospital, No. 1277 Jiefang Road, Jianghan District, Wuhan 430000, People’s Republic of ChinaEmail ziqiao_lei@hust.edu.cnPurpose: The low sensitivity and false-negative results of nucleic acid testing greatly affect its performance in diagnosing and discharging patients with coronavirus disease (COVID-19). Chest computed tomography (CT)-based evaluation of pneumonia may indicate a need for isolation. Therefore, this radiologic modality plays an important role in managing patients with suspected COVID-19. Meanwhile, deep learning (DL) technology has been successful in detecting various imaging features of chest CT. This study applied a novel DL technique to standardize the discharge criteria of COVID-19 patients with consecutive negative respiratory pathogen nucleic acid test results at a “square cabin” hospital.Patients and Methods: DL was used to evaluate the chest CT scans of 270 hospitalized COVID-19 patients who had two consecutive negative nucleic acid tests (sampling interval > 1 day). The CT scans evaluated were obtained after the patients’ second negative test result. The standard criterion determined by DL for patient discharge was a total volume ratio of lesion to lung < 50%.Results: The mean number of days between hospitalization and DL was 14.3 (± 2.4). The average intersection over union was 0.7894. Two hundred and thirteen (78.9%) patients exhibited pneumonia, of whom 54.0% (115/213) had mild interstitial fibrosis. Twenty-one, 33, and 4 cases exhibited vascular enlargement, pleural thickening, and mediastinal lymphadenopathy, respectively. Of the latter, 18.8% (40/213) had a total volume ratio of lesions to lung ≥ 50% according to our severity scale and were monitored continuously in the hospital. Three cases had a positive follow-up nucleic acid test during hospitalization. None of the 230 discharged cases later tested positive or exhibited pneumonia progression.Conclusion: The novel DL enables the accurate management of hospitalized patients with COVID-19 and can help avoid cluster transmission or exacerbation in patients with false-negative acid test.Keywords: X-ray, computed tomography, SARS-CoV-2, infectious disease, lung disease
format	article
author	Meng Q Liu W Gao P Zhang J Sun A Ding J Liu H Lei Z
author_facet	Meng Q Liu W Gao P Zhang J Sun A Ding J Liu H Lei Z
author_sort	Meng Q
title	Novel Deep Learning Technique Used in Management and Discharge of Hospitalized Patients with COVID-19 in China
title_short	Novel Deep Learning Technique Used in Management and Discharge of Hospitalized Patients with COVID-19 in China
title_full	Novel Deep Learning Technique Used in Management and Discharge of Hospitalized Patients with COVID-19 in China
title_fullStr	Novel Deep Learning Technique Used in Management and Discharge of Hospitalized Patients with COVID-19 in China
title_full_unstemmed	Novel Deep Learning Technique Used in Management and Discharge of Hospitalized Patients with COVID-19 in China
title_sort	novel deep learning technique used in management and discharge of hospitalized patients with covid-19 in china
publisher	Dove Medical Press
publishDate	2020
url	https://doaj.org/article/87cfb874fb9849bdaadb535f5092d3e5
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Novel Deep Learning Technique Used in Management and Discharge of Hospitalized Patients with COVID-19 in China

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