Background Recently, artificial intelligence (AI)-based applications for chest imaging have emerged as potential tools to assist clinicians in the diagnosis and management of patients with coronavirus disease 2019 (COVID-19). Objectives To develop a deep learning-based clinical decision support system for automatic diagnosis of COVID-19 on chest CT scans. Secondarily, to develop a complementary segmentation tool to assess the extent of lung involvement and measure disease severity. Methods The Imaging COVID-19 AI initiative was formed to conduct a retrospective multicentre cohort study including 20 institutions from seven different European countries. Patients with suspected or known COVID-19 who underwent a chest CT were included. The dataset was split on the institution-level to allow external evaluation. Data annotation was performed by 34 radiologists/radiology residents and included quality control measures. A multi-class classification model was created using a custom 3D convolutional neural network. For the segmentation task, a UNET-like architecture with a backbone Residual Network (ResNet-34) was selected. Results A total of 2,802 CT scans were included (2,667 unique patients, mean [standard deviation] age = 64.6 [16.2] years, male/female ratio 1.3:1). The distribution of classes (COVID-19/Other type of pulmonary infection/No imaging signs of infection) was 1,490 (53.2%), 402 (14.3%), and 910 (32.5%), respectively. On the external test dataset, the diagnostic multiclassification model yielded high micro-average and macro-average AUC values (0.93 and 0.91, respectively). The model provided the likelihood of COVID-19 vs other cases with a sensitivity of 87% and a specificity of 94%. The segmentation performance was moderate with Dice similarity coefficient (DSC) of 0.59. An imaging analysis pipeline was developed that returned a quantitative report to the user. Conclusion We developed a deep learning-based clinical decision support system that could become an efficient concurrent reading tool to assist clinicians, utilising a newly created European dataset including more than 2,800 CT scans.

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A deep learning-based application for COVID-19 diagnosis on CT: The Imaging COVID-19 AI initiative

Diagnostic performance of two commercially available coronavirus disease 2019 antibody kits: Elecsys Anti-SARS CoV-2 (Roche) and Anti-SARS-CoV-2 ELISA (IgG) (Euroimmun)

BACKGROUND
The lung is one of the major organs affected by the SARS-CoV-2 virus. Lung CT scan and RT-PCR are the most valuable diagnostic methods in the early diagnosis and management of COVID-19. Due to the possible inconsistency of the false-negative results for the RT-PCR test, in this study, we aimed to evaluate the sensitivity and specificity of lung CT-scan as an accurate diagnostic method of COVID-19.


METHOD
In this cross-sectional study, patients suspected of COVID-19 and referred to Shahid Beheshti Hospital in Qom city from February 26 to April 13, 2020, were enrolled. For a definitive diagnosis of COVID-19, chest CT scan and RT-PCR testing was performed for 644 patients, and both sensitivity and specificity of lung CT scan were evaluated.


RESULTS
According to the findings, and comparing to the RT-PCR test as the standard gold, sensitivity, specificity as well as, positive predictive and negative predictive values of lung CT-scan were found as follow; 94.47% (95% CI:90.73 - 97.02%), 24.71% (95% CI:20.70 - 29.07%), 40.73% (95% CI:36.58 - 44.99%), 89.08% (95% CI: 82.4 - 94.05%), respectively.


CONCLUSION
According to the findings, the lung CT scan has a better diagnostic value than RT-PCR in symptomatic patients who were referred to the hospital for COVID-19 diagnosis. Performing lung CT-scan in patients with negative RT-PCR test should be assessed.

Comparing the sensitivity and specificity of lung CT-scan with RT-PCR for diagnosis of COVID-19.

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Comparing the sensitivity and specificity of lung CT-scan with RT-PCR for diagnosis of COVID-19.