Abstract
ObjectiveThe 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.MethodsWe 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.ResultsThe 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%.ConclusionThese results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis.Key Points• 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.
Highlights
The outbreak of atypical and person-to-person transmissible pneumonia caused by the severe acute respiratory syndrome corona virus 2 (SARS-COV-2, known as 2019-nCov) has caused a global pandemic
The model was used to distinguish between COVID-19 and other typical viral pneumonia, both of which have quite similar radiologic characteristics
Once someone is identified as a person under investigation (PUI), lower respiratory specimens, such as bronchoalveolar lavage, tracheal aspirate, or sputum, will be collected for pathogenic testing
Summary
The outbreak of atypical and person-to-person transmissible pneumonia caused by the severe acute respiratory syndrome corona virus 2 (SARS-COV-2, known as 2019-nCov) has caused a global pandemic. The clinical characteristics of COVID-19 include respiratory symptoms, fever, cough, dyspnea, and pneumonia [3,4,5,6] These symptoms are nonspecific, as there are isolated cases wherein, for example, in an asymptomaticinfected family, a chest CT scan revealed pneumonia and the pathogenic test for the virus reported a positive result. Once someone is identified as a person under investigation (PUI), lower respiratory specimens, such as bronchoalveolar lavage, tracheal aspirate, or sputum, will be collected for pathogenic testing. This laboratory technology is based on real-time RT-PCR and sequencing of nucleic acids from the virus [7, 8]. Conservative estimates of the detection rate of nucleic acid are low (30–50%) [9], and the tests must be repeated several times in many cases before the results are confirmed
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
