Pneumonia, ViralNCT06501599
AI-based System for Assessing Suspected Viral Pneumonia Related Lung Changes
An observational study of Pneumonia, Viral and COVID-19 Pneumonia, run by Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department at 1 site in Russia.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Pneumonia, Viral, COVID-19 Pneumonia
- What is being tested?
- Medical software (AI-based system)
- How many people
- 563 participants
- Where
- 1 siteRussia
- Study dates
- 3 Jun 2024to 3 Dec 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- 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;
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- 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
- 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.
Who cannot join
- 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;
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- 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.
Trial locations and contacts
1 site in 1 country.
Anton Vladzymyrskyy contact
Russia1 site, 1 recruiting
- Recruiting
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
Moscow, 127051
About this study
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
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.
Read the detailed description
The AI-based system designed to process chest 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).
This retrospective clinical study will provide the clinical validation of the AI-based system to analyze chest CT images and identify pathological patterns associated with interstitial changes in pneumonia. Clinical validation measures (sensitivity, specificity, accuracy, and area under the ROC curve) will be determined and compared with values declared by the manufacturer to provide evidence about the clinical efficacy of the AI-based system.
The first stage of clinical validation is the collection of a verified labeled dataset. For this purpose, the dataset is collected, labeled, and verified by a research group. The verified dataset should include chest CT images without infiltrative and interstitial lung changes characteristic of viral pneumonia, including COVID-19-associated (CT-0) and chest CT images of all degrees of lung involvement CT-1 (≤25%), CT-2 (25-50%), CT-3 (50-75%), CT-4 (≥75%) [1]. Forming the verified dataset will allow reliable conclusions to be drawn upon completion of the clinical validation. The verified dataset must include a sufficient volume of chest CT images. The verified dataset must be de-identified to ensure the safety of patient personal data.
The second stage of the clinical validation is assessing AI-based system performance by experts. For that purpose, the AI software is analyzed to identify radiological signs of viral pneumonia. Then an examination is made of the correctness of the quantitative assessment of lung damage associated with interstitial changes in pneumonia. The evaluation of both the ability to correctly identify signs of lung damage and to quantify the identified changes is carried out on the same verified dataset.
The third stage of clinical validation is the calculation of clinical efficacy metrics (accuracy, sensitivity, specificity, area under the ROC-curve (AUROC) of the AI-based system by testing it on a verified data set. Testing of the hypothesis to verify the main diagnostic characteristics (sensitivity and specificity) declared by the manufacturer is planned by constructing a two-sided 95% confidence interval (CI), which should not differ by more than 8% from the declared values of 95% and 97%, respectively. Those. the lower limit of the 95% CI for sensitivity should not cross the 87% threshold, and the lower limit of the 95% CI for specificity should not cross the 89% threshold.
All stages of the clinical trial must be under the control of the Principal Investigator.
Randomization of images is not provided in this clinical study, because All CT images will be assessed by the research group and AI software. Also, this design does not involve blinding or masking of the research team. The evaluation of CT images by experts and the software is carried out independently, i.e. the results of each party's assessment are not known to the other party in advance.
- Conditions
- Pneumonia, Viral, COVID-19 Pneumonia
- Being tested
- Medical software (AI-based system)
- Keywords
- Artificial Intelligence, Machine Learning, Computed tomography, Chest CT
- Medical subject headings
- Pneumonia, Viral
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Medical software (AI-based system)Diagnostic test
Retrospective analysis of chest CT images with medical software (AI-based system)
| Study group | Role | What they receive |
|---|---|---|
| Normal | CT-0. Not consistent with pneumonia (including COVID-19). [1,2]
Enrollment number: 95 Receives: 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 Receives: Diagnostic Test: Medical software (AI-based system) | |
| Moderate | CT-2. Ground glass opacities. Pulmonary parenchymal involvement 25-50% [1,2].
Enrollment number: 117 Receives: 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 Receives: 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 Receives: Diagnostic Test: Medical software (AI-based system) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy
The ability of an AI-based system to produce the correct result relative to the total number of trials
Measured: 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
Measured: 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
Measured: 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
Measured: Upon completion, up to 1 year
Secondary outcomes (1)
Approximate volume of affected lung tissue
Approximate volume of affected lung tissue - quantitative characteristics of lung damage volume in percent (%): separately for left lung, right lung and total percentage of damage
Measured: Time Frame: Upon completion, up to 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Jul 2024
- Study start3 Jun 2024
- Primary completion3 Jun 2025 (estimated)
- Study completion3 Dec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Pneumonia, ViralStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 563 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
- Sponsor type
- University, hospital or other
- Collaborators
- Sciberia Co. Ltd
- Responsible party
- Principal investigator, Anton V. Vladzymyrskyy, Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06501599
- Sponsor's protocol ID
- 2024-1
- Status last verified
- Jul 2024
- First submitted
- 9 Jul 2024
- Last updated
- 22 Jul 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 22 Jul 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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
- ClinicalTrials.gov ID
- NCT06501599
- Acronym / sponsor ID
- 2024-1
- Conditions
- Pneumonia, Viral, COVID-19 Pneumonia
- Keywords
- Artificial Intelligence, Machine Learning, Computed tomography, Chest CT
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 563 participants (estimated)
- Lead sponsor
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care DepartmentUniversity, hospital or other
- Collaborators
- Sciberia Co. Ltd
- Study start
- 3 Jun 2024 (actual)
- Primary completion
- 3 Jun 2025 (estimated)
- Study completion
- 3 Dec 2025 (estimated)
- First posted
- 15 Jul 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
The AI-based system designed to process chest 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).
This retrospective clinical study will provide the clinical validation of the AI-based system to analyze chest CT images and identify pathological patterns associated with interstitial changes in pneumonia. Clinical validation measures (sensitivity, specificity, accuracy, and area under the ROC curve) will be determined and compared with values declared by the manufacturer to provide evidence about the clinical efficacy of the AI-based system.
The first stage of clinical validation is the collection of a verified labeled dataset. For this purpose, the dataset is collected, labeled, and verified by a research group. The verified dataset should include chest CT images without infiltrative and interstitial lung changes characteristic of viral pneumonia, including COVID-19-associated (CT-0) and chest CT images of all degrees of lung involvement CT-1 (≤25%), CT-2 (25-50%), CT-3 (50-75%), CT-4 (≥75%) [1]. Forming the verified dataset will allow reliable conclusions to be drawn upon completion of the clinical validation. The verified dataset must include a sufficient volume of chest CT images. The verified dataset must be de-identified to ensure the safety of patient personal data.
The second stage of the clinical validation is assessing AI-based system performance by experts. For that purpose, the AI software is analyzed to identify radiological signs of viral pneumonia. Then an examination is made of the correctness of the quantitative assessment of lung damage associated with interstitial changes in pneumonia. The evaluation of both the ability to correctly identify signs of lung damage and to quantify the identified changes is carried out on the same verified dataset.
The third stage of clinical validation is the calculation of clinical efficacy metrics (accuracy, sensitivity, specificity, area under the ROC-curve (AUROC) of the AI-based system by testing it on a verified data set. Testing of the hypothesis to verify the main diagnostic characteristics (sensitivity and specificity) declared by the manufacturer is planned by constructing a two-sided 95% confidence interval (CI), which should not differ by more than 8% from the declared values of 95% and 97%, respectively. Those. the lower limit of the 95% CI for sensitivity should not cross the 87% threshold, and the lower limit of the 95% CI for specificity should not cross the 89% threshold.
All stages of the clinical trial must be under the control of the Principal Investigator.
Randomization of images is not provided in this clinical study, because All CT images will be assessed by the research group and AI software. Also, this design does not involve blinding or masking of the research team. The evaluation of CT images by experts and the software is carried out independently, i.e. the results of each party's assessment are not known to the other party in advance.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 5
| Arm | Type | Receives |
|---|---|---|
| Normal | – | Diagnostic Test: Medical software (AI-based system)CT-0. Not consistent with pneumonia (including COVID-19). [1,2] Enrollment number: 95 |
| Mild | – | Diagnostic Test: Medical software (AI-based system)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 |
| Moderate | – | Diagnostic Test: Medical software (AI-based system)CT-2. Ground glass opacities. Pulmonary parenchymal involvement 25-50% [1,2]. Enrollment number: 117 |
| Severe | – | Diagnostic Test: Medical software (AI-based system)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 |
| Critical | – | Diagnostic Test: Medical software (AI-based system)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 |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (15 listed)
- General
- For group Normal
- For groups Mild, Moderate, Severe, and Critical
Key exclusion criteria (9 listed)
- 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;
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Medical software (AI-based system)Retrospective analysis of chest CT images with medical software (AI-based system) | Diagnostic test | Critical, Mild, Moderate, Normal, Severe |
Primary outcome
Accuracy
Time frame: Upon completion, up to 1 year
The ability of an AI-based system to produce the correct result relative to the total number of trials
Sensitivity
Time frame: Upon completion, up to 1 year
Effectiveness of the AI-based system to correctly identifies patients with the suspected viral pneumonia related lung changes
Specificity
Time frame: Upon completion, up to 1 year
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
AUC ROC
Time frame: Upon completion, up to 1 year
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI-based system in prediction of suspected viral pneumonia related lung changes
1 secondary outcome
Approximate volume of affected lung tissue
Time frame: Time Frame: Upon completion, up to 1 year
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Jul 2024.
- 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-Link provided by the sponsor.
- 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. -Link provided by the sponsor.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Anton Vladzymyrskyy contact
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Pneumonia, Viral and COVID-19 Pneumonia, run by Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department at 1 site in Russia.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 15 inclusion and 9 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 22 Jul 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Russia. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Victoria Zinchenko (+7 (495) 276-04-36, ZinchenkoVV1@zdrav.mos.ru), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy (time frame: Upon completion, up to 1 year). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 Jul 2024.