Deep Learning-Based Recognizing COVID-19 and other Common Infectious Diseases of the Lung by Chest CT Scan Images
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Abstract
Purpose
COVID-19 has become global threaten. CT acts as an important method of diagnosis. However, human–based interpretation of CT imaging is time consuming. More than that, substantial inter-observer-variation cannot be ignored. We aim at developing a diagnostic tool for artificial intelligence (AI)–based classification of CT images for recognizing COVID-19 and other common infectious diseases of the lung.
Experimental Design
In this study, images were retrospectively collected and prospectively analyzed using machine learning. CT scan images of the lung that show or do not show COVID-19 were used to train and validate a classification framework based on convolutional neural network. Five conditions including COVID-19 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, pulmonary tuberculosis, and normal lung were evaluated. Training and validation set of images were collected from Wuhan Jin Yin-Tan Hospital whereas test set of images were collected from Zhongshan Hospital Xiamen University and the fifth Hospital of Wuhan.
Results
Accuracy, sensitivity, and specificity of the AI framework were reported. For test dataset, accuracies for recognizing normal lung, COVID-19 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, and pulmonary tuberculosis were 99.4%, 98.8%, 98.5%, 98.3%, and 98.6%, respectively. For the test dataset, accuracy, sensitivity, specificity, PPV, and NPV of recognizing COVID-19 were 98.8%, 98.2%, 98.9%, 94.5%, and 99.7%, respectively.
Conclusions
The performance of the proposed AI framework has excellent performance of recognizing COVID-19 and other common infectious diseases of the lung, which also has balanced sensitivity and specificity.
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SciScore for 10.1101/2020.03.28.20046045: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: This study was approved by the Ethics Commission of Zhongshan Hospital Xiamen University.
Consent: Written informed consent was waived by the Ethics Commission of the designated hospital because of non-interventional study and no identifiable personal information was recorded.Randomization Datasets: Lung CT scan images collected from Department of radiology, Wuhan Jin Yin-Tan Hospital were randomly divided into training set or validation set at a ratio of 3:1. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Training and validation the algorithm: Based on deep … SciScore for 10.1101/2020.03.28.20046045: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: This study was approved by the Ethics Commission of Zhongshan Hospital Xiamen University.
Consent: Written informed consent was waived by the Ethics Commission of the designated hospital because of non-interventional study and no identifiable personal information was recorded.Randomization Datasets: Lung CT scan images collected from Department of radiology, Wuhan Jin Yin-Tan Hospital were randomly divided into training set or validation set at a ratio of 3:1. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Training and validation the algorithm: Based on deep learning, we used the PyTorch platform to adopt the ResNet-50 architecture pretrained using the ImageNet dataset(7) to develop our AI algorithm. ImageNetsuggested: (VGG-16, RRID:SCR_016494)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:The limitation is that our AI framework will need further evaluation by more wide clinical application.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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