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RECRUITING

NCT06501599

AI-based System for Assessing Suspected Viral Pneumonia Related Lung Changes

Sponsor: Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department

View on ClinicalTrials.gov

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.

Official title: Artificial Intelligence Based System for Assessing Suspected Viral Pneumonia Related Lung Changes According to Visual Pulmonary Lesion Grading System (CT 0-4): Retrospective Study

Key Details

Gender

All

Age Range

18 Years - Any

Study Type

OBSERVATIONAL

Enrollment

563

Start Date

2024-06-03

Completion Date

2025-12-03

Last Updated

2024-07-22

Healthy Volunteers

Yes

Conditions

Pneumonia, Viral COVID-19 Pneumonia

Interventions

DIAGNOSTIC_TEST

Medical software (AI-based system)

Retrospective analysis of chest CT images with medical software (AI-based system)

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 pattern: ground glass opacities (mild, moderate, and higher intensity); 2. Patients who contain COVID-19-related CT pattern: pulmonary consolidation; 3. Patients who contain COVID-19-related CT pattern: cobblestone infiltration of the lung parenchyma; 4. Patients who contain COVID-19-related CT pattern: hydrothorax; 5. Patients who contain a combination of one or more patterns. Exclusion Criteria: * Patients whose studies contain images with unreported CT patterns; * Patients whose examinations do not conform to DICOM format; * Patients whose examinations do not contain imaging of the lung region * Patients whose examinations contain technical artifacts caused by malfunctions or features of CT scanners; * Patients whose examinations contain improper patient positioning; * Patients whose examinations contain studies with deleted DICOM tags responsible for scan size and image parameters; * Patients whose examinations contain metal artifacts on the patient's body and clothing; * Patients whose examinations contain the presence of other pathologic changes of lungs in patients - neoplastic, tuberculosis process, bacterial pneumonia, etc.; * Patients under 18 years old.

Locations (1)

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department

Moscow, Russia