Tuberculosis, PulmonaryNCT05889364
AI-based System for Lung Tuberculosis Screening: Diagnostic Accuracy Evaluation
An observational study of Tuberculosis, Pulmonary, 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
Testing of AI solutions to assess diagnostic accuracy for tuberculosis detection. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Tuberculosis, Pulmonary
- What is being tested?
- AI-based x-ray analysis and triage ("normal/tuberculosis suspected")
- How many people
- 308 participants
- Where
- 1 siteRussia
- Study dates
- 1 Feb 2018to 30 Dec 2023
- 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 to 80 years
- All sexes
- Adult, Older adult
Study population: 308 chest x-ray images selected from the database of Unified Radiological Information Service (URIS) of Moscow
Who may be able to join
- no pathology in a lung on chest x-ray
- signs of lung tuberculosis on chest x-ray
Who cannot join
- any pathology in the lungs (except tuberculosis)
Trial locations and contacts
1 site in 1 country.
Russia1 site
Research and Practical Center of Medical Radiology, Department of Health Care of Moscow
Moscow, 109029
About this study
Testing of AI solutions to assess diagnostic accuracy for tuberculosis detection.
Read the detailed description
Tuberculosis remains a key problem of modern medicine. New approaches for burden overcoming should be proposed. New screening strategies may include artificial intelligence (AI). An AI-based system for chest x-ray analysis and triage ("normal/tuberculosis suspected") have been developed and trained. A special data-set was prepared. There are 238 normal x-rays and 70 x-rays with lung tuberculosis in data-set. The data-set was randomly divided into 2 samples:
- sample N1 (n=140) with ratio "normal: tuberculosis" 50:50,
- sample N1 (n=150) with ratio "normal: tuberculosis" 95:5. Both samples will be analysed by AI-based system. Results will be quantified using diagnostic accuracy metrics: sensitivity and specificity, positive and negative predictor values, likelihood ratio, and area under the ROC (receiver operating characteristic) curve.
- Conditions
- Tuberculosis, Pulmonary
- Being tested
- AI-based x-ray analysis and triage ("normal/tuberculosis suspected")
- Keywords
- AI, deep learning, tuberculosis, radiology, x-ray, e-health, screening, chest, artificial intelligence
- Medical subject headings
- Tuberculosis, Pulmonary, Tuberculosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-based x-ray analysis and triage ("normal/tuberculosis suspected")Diagnostic test
Also called artificial intelligence analysis of medical images
All included x-rays will be analysed by the AI-based system. Then results will be compared with opinions of 2 experienced radiologists (they make peer-review of all included images independently of each other).
| Study group | Role | What they receive |
|---|---|---|
| Sample N1 | (n=140) with ratio "normal: tuberculosis" 50:50 Receives: Diagnostic Test: AI-based x-ray analysis and triage ("normal/tuberculosis suspected") | |
| Sample N2 | (n=150) with ratio "normal: tuberculosis" 95:5 Receives: Diagnostic Test: AI-based x-ray analysis and triage ("normal/tuberculosis suspected") |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic accuracy metric 1
Sensitivity
Measured: Day 1 upon receipt of data
Diagnostic accuracy metric 2
Specificity
Measured: Day 2 upon receipt of data
Diagnostic accuracy metric 3
Positive predictor values
Measured: Day 3 upon receipt of data
Diagnostic accuracy metric 4
Negative predictor values
Measured: Day 4 upon receipt of data
Diagnostic accuracy metric 5
Likelihood ratio
Measured: Day 5 upon receipt of data
Diagnostic accuracy metric 6
Area under the ROC curve
Measured: Day 6 upon receipt of data
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Jun 2023
- Study start1 Feb 2018
- Primary completion15 Mar 2018
- Study completion30 Dec 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Tuberculosis, PulmonaryStudies 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
- Other
- Time perspective
- Retrospective
- Enrolment
- 308 participants (actual)
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
- Responsible party
- Principal investigator, Anton V. Vladzymyrskyy, Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
- Investigators
- Anton Vladzymyrskyy, Principal investigator, Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05889364
- Sponsor's protocol ID
- 2018-1
- Status last verified
- May 2023
- First submitted
- 28 Mar 2018
- Last updated
- 5 Jun 2023
- Results posted
- No
- Data monitoring committee
- No
- 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 5 Jun 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-based System for Lung Tuberculosis Screening: Diagnostic Accuracy Evaluation
- ClinicalTrials.gov ID
- NCT05889364
- Acronym / sponsor ID
- 2018-1
- Conditions
- Tuberculosis, Pulmonary
- Keywords
- AI, deep learning, tuberculosis, radiology, x-ray, e-health, screening, chest
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 308 participants (actual)
- Lead sponsor
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care DepartmentUniversity, hospital or other
- Study start
- 1 Feb 2018 (actual)
- Primary completion
- 15 Mar 2018 (actual)
- Study completion
- 30 Dec 2023 (estimated)
- First posted
- 5 Jun 2023 (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.
Testing of AI solutions to assess diagnostic accuracy for tuberculosis detection.
Detailed description
Tuberculosis remains a key problem of modern medicine. New approaches for burden overcoming should be proposed. New screening strategies may include artificial intelligence (AI). An AI-based system for chest x-ray analysis and triage ("normal/tuberculosis suspected") have been developed and trained. A special data-set was prepared. There are 238 normal x-rays and 70 x-rays with lung tuberculosis in data-set. The data-set was randomly divided into 2 samples:
- sample N1 (n=140) with ratio "normal: tuberculosis" 50:50,
- sample N1 (n=150) with ratio "normal: tuberculosis" 95:5. Both samples will be analysed by AI-based system. Results will be quantified using diagnostic accuracy metrics: sensitivity and specificity, positive and negative predictor values, likelihood ratio, and area under the ROC (receiver operating characteristic) curve.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Sample N1 | – | Diagnostic Test: AI-based x-ray analysis and triage ("normal/tuberculosis suspected")(n=140) with ratio "normal: tuberculosis" 50:50 |
| Sample N2 | – | Diagnostic Test: AI-based x-ray analysis and triage ("normal/tuberculosis suspected")(n=150) with ratio "normal: tuberculosis" 95:5 |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- 308 chest x-ray images selected from the database of Unified Radiological Information Service (URIS) of Moscow
Key inclusion criteria (2 listed)
- no pathology in a lung on chest x-ray
- signs of lung tuberculosis on chest x-ray
Key exclusion criteria (1 listed)
- any pathology in the lungs (except tuberculosis)
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 |
|---|---|---|
| AI-based x-ray analysis and triage ("normal/tuberculosis suspected")Also: artificial intelligence analysis of medical imagesAll included x-rays will be analysed by the AI-based system. Then results will be compared with opinions of 2 experienced radiologists (they make peer-review of all included images independently of each other). | Diagnostic test | Sample N1, Sample N2 |
Primary outcome
Diagnostic accuracy metric 1
Time frame: Day 1 upon receipt of data
Sensitivity
Diagnostic accuracy metric 2
Time frame: Day 2 upon receipt of data
Specificity
Diagnostic accuracy metric 3
Time frame: Day 3 upon receipt of data
Positive predictor values
Diagnostic accuracy metric 4
Time frame: Day 4 upon receipt of data
Negative predictor values
Diagnostic accuracy metric 5
Time frame: Day 5 upon receipt of data
Likelihood ratio
Diagnostic accuracy metric 6
Time frame: Day 6 upon receipt of data
Area under the ROC curve
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 Jun 2023.
- Individual participant dataSharing planned: No
Related trials for Tuberculosis, Pulmonary
RecruitingPhase 3
A Study of Quabodepistat-containing Regimens for the Treatment of Drug-resistant Pulmonary Tuberculosis
Rifampicin-Resistant Pulmonary Tuberculosis, Multidrug-Resistant Pulmonary Tuberculosis
RecruitingPhase 2
BRIGHT-Child TB: Short-Course Treatment for Non-Severe Pulmonary Tuberculosis in Children
Tuberculosis
Recruiting
Channa Striata and Curcuma Domestica Supplementation in Patients With Active Pulmonary Tuberculosis
Pulmonary Tuberculosis
RecruitingPhase 2
Trial of Novel Regimens for the Treatment of Pulmonary Tuberculosis
Pulmonary Tuberculosis
RecruitingPhase 2
Platform Assessing Regimens and Durations In a Global Multisite Consortium for TB
Pulmonary Tuberculosis
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 principal investigator
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
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 Tuberculosis, Pulmonary, 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 2 inclusion and 1 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 5 Jun 2023. 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?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic accuracy metric 1 (time frame: Day 1 upon receipt of data). The study lists 6 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 5 Jun 2023; AzTrials last refreshed its copy on 28 Oct 2026.