5Background: Computed tomography (CT) is the preferred imaging method for diagnosing 2019 4 6 novel coronavirus (COVID19) pneumonia. Our research aimed to construct a system based on 4 7 deep learning for detecting COVID-19 pneumonia on high resolution CT, relieve working 4 8 pressure of radiologists and contribute to the control of the epidemic. 4 9

Graphical abstract

Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Twenty-seven prospective consecutive patients in Renmin Hospital of Wuhan University were collected to evaluate the efficiency of radiologists against 2019-CoV pneumonia with that of the model. An external test was conducted in Qianjiang Central Hospital to estimate the system’s robustness. The model achieved a per-patient accuracy of 95.24% and a per-image accuracy of 98.85% in internal retrospective dataset. For 27 internal prospective patients, the system achieved a comparable performance to that of expert radiologist. In external dataset, it achieved an accuracy of 96%. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice.

These authors contributed equally to this work. ⇤ Corresponding authors.The sudden outbreak of novel coronavirus 2019 (COVID-19) increased the diagnostic burden of radiologists. In the time of an epidemic crisis, we hoped artificial intelligence (AI) to help reduce physician workload in regions with the outbreak, and improve the diagnosis accuracy for physicians before they could acquire enough experience with the new disease. Here, we present our experience in building and deploying an AI system that automatically analyzes CT images to detect COVID-19 pneumonia features. Different from conventional medical AI, we were dealing with an epidemic crisis. Working in an interdisciplinary team of over 30 people with medical and / or AI background, geographically distributed in Beijing and Wuhan, we were able to overcome a series of challenges in this particular situation and deploy the system in four weeks. Using 1,136 training cases (723 positives for COVID-19) from five hospitals, we were able to achieve a sensitivity of 0.974 and specificity of 0.922 on the test dataset, which included a variety of pulmonary diseases. Besides, the system automatically highlighted all lesion regions for faster examination. As of today, we have deployed the system 2 All rights reserved. No reuse allowed without permission.

statement: In patients suspected of having COVID-19, the initial chest CT showed high sensitivity, but low specificity. Key Results:(1) The sensitivity, specificity, positive predictive value, and negative predictive value (with 95% confidence intervals) were 93% (85-97%), 53% (27-77%), 92% (83-96%), 42% (18-70%), respectively. Similar results were shown in both geographic regions studied.(2) There were no significant differences in the distribution of positive rates of tests in the two geographic regions between CT (P=0.423) and reverse transcription polymerase chain reaction testing (P=0.931). AbbreviationsPPV= positive predictive value (PPV) NPV= negative predictive value (NPV) 95% CI=95% confidence interval RT-PCR = reverse transcription polymerase chain reaction CT=computed tomography (CT) Abstract Background: Coronavirus disease 2019 (COVID-19) is a new viral respiratory disease that has recently emerged from China, becoming a pandemic. However, few studies have analyzed 3data regarding the clinical performance of chest computed tomography (CT) obtained in subjects with suspected COVID-19 at the initial presentation to medical facilities. Objective:The purpose of the present study was to evaluate the performance of chest CT the initial presentation of patients with suspected COVID-19.Methods: Data from 103 patients who were under investigation for COVID-19 based on inclusion criteria according to WHO Interim Guidance were retrospectively collected from January 21, 2020 to February 14, 2020. All patients underwent chest CT scanning and reverse transcription polymerase chain reaction testing (RT-PCR) for COVID-19 at hospital presentation. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) (with 95% confidence intervals) were calculated to evaluate the performance of CT. Subgroup analyses were also performed based on the geographical distribution of these cases in the province of Henan, China.Results: There were 88 /103 (85%) patients with COVID-19 confirmed by RT-PCR. The overall sensitivity, specificity, PPV, and NPV were 93% (85-97%), 53% (27-77%), 92% (83-96%), and 42% (18-70%), respectively. Similar results were shown in both geographic regions. The respective sensitivity, specificity, PPV, and NPV for chest CT in the districts of Xinyang and Zhumadian (n = 56) were 92% (80-97%), 63% (26-90%), 93% (81-98%), and 56% (23-85%), while these indicators in the district of Anyang (n = 47) were 95% (81-99%), 43% (12-80%), 90% (76-97%), and 60% (17-93%). There were no significant differences in the prevalence of positive exams in the two geographic subgroups for CT (P=0.423) or RT-PCR (P=0.931). 4Conclusion: Although initial chest CT obtained at hospital presentation showed high sensitivity in patients under investigation for COVID-19 in the two geographic regions in Henan province, the NPV was only modest, suggesting low value of CT as a screening tool.