A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)

By Shuai Wang, B. Kang, Jinlu Ma, Xianjun Zeng, M. Xiao, Jia Guo, Meng Cai, Jingyi Yang, Yaodong Li, Xiangfei Meng and 1 more. European Radiology, 2020

1,389 citations recorded by the source corpus.

The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 26 million cases of Corona virus disease (COVID-19) in the world so far. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment are a priority. Pathogenic laboratory testing is typically the gold standard, but it bears the burden of significant false negativity, adding to the urgent need of alternative diagnostic methods to combat the disease. Based on COVID-19 radiographic changes in CT images, this study hypothesized that artificial intelligence methods might be able to extract specific graphical features of COVID-19 and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. We collected 1065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the inception transfer-learning model to establish the algorithm, followed by internal and external validation. The internal validation achieved a total accuracy of 89.5% with a specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with a specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images, the first two nucleic acid test results were negative, and 46 were predicted as COVID-19 positive by the algorithm, with an accuracy of 85.2%. These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. • The study evaluated the diagnostic performance of a deep learning algorithm using CT images to screen for COVID-19 during the influenza season. • As a screening method, our model achieved a relatively high sensitivity on internal and external CT image datasets. • The model was used to distinguish between COVID-19 and other typical viral pneumonia, both of which have quite similar radiologic characteristics.

Subfields: Infectious Diseases, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/3ec772e3c23f749591cfae3d8e3492c331a18392

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Citations in the corpus

Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks (2020, methodology): “study, based on the COVID-19 radiographic changes from CT images, they have developed a deep learning method that can extract the graphical features of COVID-19 to provide clinical diagnosis prior to pathogenic testing and thus save critical time for the disease diagnosis [16] .”
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging (2020, neutral): “[57] 2D-CNN COVID-19 and viral Pneu.”
Deep learning based detection and analysis of COVID-19 on chest X-ray images (2020, methodology): “To implement a deep learning method [46] requires a large amount of dataset to receive reliable results.”
COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images (2020, background): “A number of studies have proposed deep learning systems based on chest CT imaging to distinguish COVID-19 cases from non-COVID-19 cases (which may include both normal and abnormal cases) [16, 20, 21, 22, 23, 24, 25, 26, 27, 17, 28, 29, 30].”
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19 (2020, methodology): “A 2D CNN model is proposed in [76] on manually delineated region patches to classify between COVID-19 and typical”
Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia (2020, background): “Some recent works have focused on the COVID-19 diagnosis from other pneumonia in CT images [39]–[41].”
Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks (2020, methodology): “38% obtained from the proposed model, which is better than [8, 9].”
Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays (2020, neutral): “Researchers in [40] propose also a deep learning model for COVID-19 detection.”
Student’s Responses on Learning in the Early COVID-19 Pandemic (2020, background): “This outbreak has caused approximately 64,000 cases of COVID-19 in China so far, and with that number continuing to grow (Wang et al., 2020).”
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network (2020, methodology): “For example, in [33], a CNN was applied based on Inception network to detect COVID-19 disease within computed tomography (CT ).”
Can AI Help in Screening Viral and COVID-19 Pneumonia? (2020, methodology): “[39] used deep learning techniques on CT images to screen COVID-19 patients with an accuracy, specificity and sensitivity of 89.”
Improving performance of CNN to predict likelihood of COVID-19 using chest X-ray images with preprocessing algorithms (2020, methodology): “Wang et al. in [10] also used a transfer learning based CNN deep learning model to classify COVID-19 using CT images.”
Automated detection of COVID-19 cases using deep neural networks with X-ray images (2020, neutral): “[47] achieved a classification accuracy of 82.”
COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data (2020, result): “Several studies have shown promising results in using deep learning models to automated diagnosis of COVID-19 from CT images [6], [11], [12].”