Aims: The effects of the COVID-19 pandemic continue around the world. Imaging methods play an important role in the diagnosis of COVID-19. The aim of this study was to develop a system that would allow for the distinguishing of lesions at different stages of the disease based on similar signs of other viral diseases and monitoring the emergence, progression, and/or remission of lesions in different areas of the lungs. Methods: For the deep learning (DL) system, the thoracic CT images from 1,382 images were reviewed. These belonged to patients whose SARS-CoV-2 RT-PCR tests turned out positive, were diagnosed with COVID-19, and had signs of lung involvement. Of 1,382 images in the dataset, 180 were assigned for testing and 1,202 were assigned for training. Apart from our dataset, 131 images for internal testing and 1,365 images for external testing were used. The trainings were continued to cover 316,000 steps. Results: Internal and external analyses were used to assess the developed model. The internal analysis success rate was 93.12%. For first external analysis we used 85 images. In the first external analysis we assessed a single CT image of each patient who was in the mixed image lists, and the success rate was found to be 70.31%. In the second external analysis, 645 thoracic CT images of patients diagnosed with COVID-19 and 635 images of another patients who had signs of non-COVID-19 diseases were used. We assessed the thoracic CT images with both COVID-19 and non-COVID-19 disease signs. The success rate in the identification of COVID-19 patients was 88.4%. Conclusion: Special modeling systems developed using DL may help accelerate workflow and making the process easier. This is especially important in cases in which fast and accurate assessment is essential for of a large number of patients, as happens in a pandemic.

Aims: Laboratory findings in COVID-19 patients vary according to the severity of the disease. This study aimed at defining a system of formulas that may predict the presence of thoracic CT involvement, the extent of such involvement and the need for intensive care stay on the basis of patient laboratory data using the Waikato Environment for Knowledge Analysis (WEKA) software. Methods: This study was conducted with 508 patients whose SARS-CoV-2 RT-PCR test was positive. These patients were divided into 2 groups, with and without thoracic CT involvement typical for COVID-19. Then, those patients who had signs of typical involvement for COVID-19 in their thoracic CT were divided into 3 groups depending on the extent of their lesions. J48 Decision Tree classification and Linear Regression methods were used on the WEKA software. The codes implemented in the Python programming language were used at the estimation, classification and testing stages. Results: Thoracic CT scans showed that lung involvement was absent in 93 of the patients, mild in 114, moderate in 115, and severe in 159. The success rates of WEKA Linear Regression Formulas calculated using laboratory values and demographic data, respectively 78.92%, 71.69% and 91%. The success rate of the J48 Decision Tree formula used to predict the presence of involvement in thoracic CT was found to be 95.95%. The success rate of the J48 Decision Tree, which was used to predict the degree of involvement in thoracic CT, was 84.39%. The success rate of the J48 Decision Tree used to predict the need for intensive care was found to be 93.06%. Conclusion: The results of this study will facilitate revealing the presence of lung involvement and identification of critical patients in the COVID-19 pandemic and particularly under circumstances and can be used effectively to ensure triage.