RETRACTED ARTICLE: Deep learning system to screen coronavirus disease 2019 pneumonia
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Deep learning system to screen coronavirus disease 2019 pneumonia Charmaine Butt 1 & Jagpal Gill 1 & David Chun 2 & Benson A. Babu 1,2
# Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Radiographic patterns on CT chest scans have shown higher sensitivity and specificity compared to RT-PCR detection of COVID-19 which, according to the WHO has a relatively low positive detection rate in the early stages. We technically review a study that compared multiple convolutional neural network (CNN) models to classify CT samples with COVID-19, Influenza viral pneumonia, or no-infection. We compare this mentioned study with one that is developed on existing 2D and 3D deeplearning models, combining them with the latest clinical understanding, and achieved an AUC of 0.996 (95%CI: 0.989–1.00) for Coronavirus vs Non-coronavirus cases per thoracic CT studies. They calculated a sensitivity of 98.2% and a specificity of 92.2%. Keywords Pandemic . Viral pneumonias . Convolutional neural networks . Deep learing
1 Introduction In December 2019 the SARS-CoV-2 zoonotic virus, originating from the Phinolophus bat, was transmitted to humans, for the first time recorded. The Huanan Seafood Wholesale Market in Wuhan City, Hubei Province, China, was the epicenter of the coronavirus disease (COVID-19) outbreak caused by SARS-CoV-2, which rapidly spread worldwide and was declared a pandemic by WHO on 11th March 2020 [1]. COVID-19 has led to complications such as acute respiratory disorder, heart problems, and secondary infections in a relatively high proportion of patients and thus significant mortality. Early detection and commencement of treatment in severe cases is key to reducing mortality [1]. Radiographic patterns on CT chest scans have shown higher sensitivity and specificity compared to RT-PCR detection of COVID-19 which, according to the WHO has a relatively low positive detection rate in the early stages. One report, of 1041 cases from China, found that sensitivity of COVID-19 detection on chest CT was 97% (95%CI, 95– 98%, 580/601 patients) based on positive RT-PCR results [2]. CT chest images for COVID-19 positive cases have a distinct radiographic pattern: ground-glass opacities, multifocal patchy consolidation, and/or interstitial changes with a
* Benson A. Babu [email protected] 1
Saint John’s Episcopal Hospital, New York, NY, USA
2
Glen Cove Northwell Health, Glen Cove, NY, USA
predominantly peripheral distribution [2, 3]. A study of 21 patients with the 2019 novel coronavirus, found that 15 (71%) had involvement of more than two lobes at chest CT, 12 (57%) had ground-glass opacities, seven (33%) had opacities with rounded morphology, seven (33%) had a peripheral distribution of disease, six (29%) had consolidation with ground-glass opacities, and four (19%) had crazy-paving pattern. No lung cavitation, discrete pulmonary nodules, pleural effusions, and lymphadenopathy were seen. Fourteen percent of patients (three of 21) presented with a normal CT scan [3]. Information and CT scans are ava
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