ABSTRACT
OBJECTIVE: To set up a differential diagnosis radiomics model to identify coronavirus disease 2019 (COVID-19) and other viral pneumonias based on an artificial intelligence (AI) approach that utilizes computed tomography (CT) images. MATERIALS AND METHODS: This retrospective multi-center research involved 225 patients with COVID-19 and 265 patients with other viral pneumonias. The least absolute shrinkage and selection operator algorithm was used for the optimized features selection from 1218 radiomics features. Finally, a logistic regression (LR) classifier was applied to construct different diagnosis models. The receiver operating characteristic curve analysis was applied to evaluate the accuracy of different models. RESULTS: The patients were divided into a training set (313 of 392, 80%), an internal test set (79 of 392, 20%) and an external test set (n = 98). Thirteen features were selected to build the machine learning-based CT radiomics models. LR classifiers performed well in the training set (area under the curve [AUC] = 0.91), internal test set (AUC = 0.94), and external test set (AUC = 0.91). Delong tests suggested there was no significant difference between training and the two test sets (P > 0.05). CONCLUSION: The use of an AI-based radiomics model enables rapid discrimination of patients with COVID-19 from other viral infections, which can aid better surveillance and control during a pneumonia outbreak.
ABSTRACT
The coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread rapidly, which now has turned into a pandemic. The new emerging infectious disease has raised many challenges and uncertainties regarding disease management and prognosis in immunocompromised patient populations. The risk of COVID-19 among people living with human immunodeficiency virus (HIV) has different opinions. Some scholars speculated that patients with HIV may be at decreased risk for complications of COVID-19 because HIV antiretroviral medications may have activity against coronaviruses such as SARS-CoV-2. But others have the opposite because of the immunosuppression for HIV patients. Here we reported a case of HIV-infected patient confirmed with COVID-19 and had a favourable prognosis. The patient was a 24-year-old male who was diagnosed with HIV infection 2 years ago and then followed a regular antiretroviral therapy (ART). After infected with COVID-19, the patient had no other clinical symptoms and laboratory abnormalities throughout the course of the disease except presented with fever for a short-term (2 days), and no secondary infection or exacerbation occurred after admission in hospital. Follow-up chest CT showed that the lung lesions disappeared within a short period of time. After standard treatment by 9 days, the patient was cured and discharged. This report highlights the importance of ART for HIV-infected persons, and with regular ART for HIV patients may reduce adverse consequences after infection with COVID-19.