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

NCT06501599 | Recruiting DMC

• 1 other identifier: 2024-1
• Study Type: observational
• Target: 563
• Locations: 1 country
• Sites: 1

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.

Conditions

Pneumonia, Viral; COVID-19 Pneumonia

Keywords

Artificial Intelligence; Machine Learning; Computed tomography; Chest CT

Outcome Measures

Primary Outcomes (4)

• Accuracy

  The ability of an AI-based system to produce the correct result relative to the total number of trials

  Upon completion, up to 1 year

• Sensitivity

  Effectiveness of the AI-based system to correctly identifies patients with the suspected viral pneumonia related lung changes

  Upon completion, up to 1 year

• Specificity

  Effectiveness of the AI-based system to correctly identifies across a range of available measurements patients that do not have the suspected viral pneumonia related lung changes

  Upon completion, up to 1 year

• AUC ROC

  The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI-based system in prediction of suspected viral pneumonia related lung changes

  Upon completion, up to 1 year

Secondary Outcomes (1)

• Approximate volume of affected lung tissue

  Time Frame: Upon completion, up to 1 year

Study Arms (5)

Normal

CT-0. Not consistent with pneumonia (including COVID-19). \[1,2\] Enrollment number: 95

Diagnostic Test: Medical software (AI-based system)

Mild

СT-1. Ground glass opacities. Pulmonary parenchymal involvement =\<25% OR absence CT signs in typical clinical manifestations and relevant epidemiological history \[1,2\]. Enrollment number: 117

Diagnostic Test: Medical software (AI-based system)

Moderate

CT-2. Ground glass opacities. Pulmonary parenchymal involvement 25-50% \[1,2\]. Enrollment number: 117

Diagnostic Test: Medical software (AI-based system)

Severe

CT-3. Ground glass opacities. Pulmonary consolidation. Pulmonary parenchymal involvement 50-75%. Lung involvement increased in 24-48 hours by 50% in respiratory impairment per follow-up studies \[1,2\]. Enrollment number: 117

Diagnostic Test: Medical software (AI-based system)

Critical

CT-4. Diffuse ground glass opacities with consolidations and reticular changes. Hydrothorax (bilateral, more on the left). Pulmonary parenchymal involvement \>=75% \[1,2\]. Enrollment number: 117

Diagnostic Test: Medical software (AI-based system)

Interventions

Medical software (AI-based system) DIAGNOSTIC_TEST

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

Critical; Mild; Moderate; Normal; Severe

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adults (patients over 18 years of age) who were referred for a chest CT study by a physician in case of suspicion or monitoring the effectiveness of treatment of diseases manifested by changes in the pulmonary parenchyma.

You may qualify if:

• General
• Patients over 18 years old;
• Patients who underwent CT without contrast enhancement;
• Patients who underwent a CT scan according to a standardized scanning protocol: 120 kilovolts, slice thickness max. 2 mm, rigid "lung" filter (kernel) reconstruction;
• Patients whose studies should be of acceptable quality, performed with breath-holding, without technical artifacts, and respiratory and motor artifacts;
• 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;
• Patients in whom the localization of changes is predominantly bilateral, in the basal and subpleural parts of the lungs, may be located peribronchial;
• For group Normal
• a. Patients who do not contain COVID-19-related CT patterns;
• For groups Mild, Moderate, Severe, and Critical
• Patients who contain COVID-19-related CT pattern: ground glass opacities (mild, moderate, and higher intensity);
• Patients who contain COVID-19-related CT pattern: pulmonary consolidation;
• Patients who contain COVID-19-related CT pattern: cobblestone infiltration of the lung parenchyma;
• Patients who contain COVID-19-related CT pattern: hydrothorax;
• Patients who contain a combination of one or more patterns.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department: lead
• Sciberia Co. Ltd: collaborator

Study Sites (1)

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

Moscow, 127051, Russia

RECRUITING

Related Links

• Morozov S.P., et al. Diagnostic accuracy of computed tomography for identifying hospitalizations for patients with COVID-19 // Digital Diagnostics. - 2021. - Vol. 2. - N. 1. - P. 5-
• Morozov S.P., et al. How does artificial intelligence effect on the assessment of lung damage in COVID-19 on chest CT scan? // Digital Diagnostics. - 2021. - Vol. 2. - N. 1. -

MeSH Terms

Conditions

Pneumonia, Viral

Condition Hierarchy (Ancestors)

Pneumonia; Respiratory Tract Infections; Infections; Virus Diseases; Lung Diseases; Respiratory Tract Diseases

Central Study Contacts

Victoria Zinchenko

CONTACT

+7 (495) 276-04-36; ZinchenkoVV1@zdrav.mos.ru

Anton Vladzymyrskyy

CONTACT

VladzimirskijAV@zdrav.mos.ru

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Chief Research Officer

Study Record Dates

• First Submitted: July 9, 2024
• First Posted: July 15, 2024
• Study Start: June 3, 2024
• Primary Completion: June 3, 2025
• Study Completion: December 3, 2025
• Last Updated: July 22, 2024

Record last verified: 2024-07

Data Sharing

• IPD Sharing: Will not share

Locations

RU (1)