Therapeutics and Clinical Risk Management (Dec 2020)
Novel Deep Learning Technique Used in Management and Discharge of Hospitalized Patients with COVID-19 in China
Abstract
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 [email protected] LeiDepartment of Radiology, The Wuhan Union Hospital, No. 1277 Jiefang Road, Jianghan District, Wuhan 430000, People’s Republic of ChinaEmail [email protected]: 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
