AI-based System for Assessing Suspected Viral Pneumonia Related Lung Changes
Trial Parameters
Brief Summary
The AI-based system designed to process chest computed tomography (CT) aims to 1) detect the presence of pathologic patterns associated with interstitial changes in pneumonia; 2) highlight areas on the images with the probable presence of pathologies; 3) provide the physician with the results of image processing, including quantitative indicators of suspected viral pneumonia related lung changes according to visual pulmonary lesion grading system (CT0-4). The retrospective study aims to demonstrate the clinical validation of the AI-based system. Clinical validation measures (sensitivity, specificity, accuracy, and area under the ROC curve) will be determined to provide evidence about the clinical efficacy of the AI-based system. The hypothesis is that the measures of clinical validation of the AI-based system differ by no more than 8% from those declared by the manufacturer.
Eligibility Criteria
Inclusion Criteria: 1. General 1. Patients over 18 years old; 2. Patients who underwent CT without contrast enhancement; 3. Patients who underwent a CT scan according to a standardized scanning protocol: 120 kilovolts, slice thickness max. 2 mm, rigid "lung" filter (kernel) reconstruction; 4. Patients whose studies should be of acceptable quality, performed with breath-holding, without technical artifacts, and respiratory and motor artifacts; 5. Patients whose studies must contain DICOM tags responsible for the orientation and position of the patient in the images during the study, as well as DICOM tags responsible for the size of the scans and image parameters; 6. Patients in whom the localization of changes is predominantly bilateral, in the basal and subpleural parts of the lungs, may be located peribronchial; 2. For group Normal a. Patients who do not contain COVID-19-related CT patterns; 3. For groups Mild, Moderate, Severe, and Critical 1. Patients who contain COVID-19-related CT