AI-based System for Lung Tuberculosis Screening: Diagnostic Accuracy Evaluation

May 25, 2023 updated by: Anton V. Vladzymyrskyy, Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
Testing of AI solutions to assess diagnostic accuracy for tuberculosis detection.

Study Overview

Status

Active, not recruiting

Conditions

Intervention / Treatment

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 Type

Observational

Enrollment (Actual)

308

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Russian Federation
      • Moscow, Russian Federation, 109029
        • Research and Practical Center of Medical Radiology, Department of Health Care of Moscow

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

N/A

Sampling Method

Non-Probability Sample

Study Population

308 chest x-ray images selected from the database of Unified Radiological Information Service (URIS) of Moscow

Description

Inclusion Criteria:

  • no pathology in a lung on chest x-ray
  • signs of lung tuberculosis on chest x-ray

Exclusion Criteria:

  • any pathology in the lungs (except tuberculosis)

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Sample N1
(n=140) with ratio "normal: tuberculosis" 50:50
Diagnostic test: AI-based x-ray analysis and triage ("normal/tuberculosis suspected")
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).
Other Names:
  • artificial intelligence analysis of medical images
Sample N2
(n=150) with ratio "normal: tuberculosis" 95:5
Diagnostic test: AI-based x-ray analysis and triage ("normal/tuberculosis suspected")
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).
Other Names:
  • artificial intelligence analysis of medical images

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Diagnostic accuracy metric 1
Time Frame: Day 1 upon receipt of data
Sensitivity
Day 1 upon receipt of data
Diagnostic accuracy metric 2
Time Frame: Day 2 upon receipt of data
Specificity
Day 2 upon receipt of data
Diagnostic accuracy metric 3
Time Frame: Day 3 upon receipt of data
Positive predictor values
Day 3 upon receipt of data
Diagnostic accuracy metric 4
Time Frame: Day 4 upon receipt of data
Negative predictor values
Day 4 upon receipt of data
Diagnostic accuracy metric 5
Time Frame: Day 5 upon receipt of data
Likelihood ratio
Day 5 upon receipt of data
Diagnostic accuracy metric 6
Time Frame: Day 6 upon receipt of data
Area under the ROC curve
Day 6 upon receipt of data

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

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

Investigators

  • Principal Investigator: Anton Vladzymyrskyy, Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

February 1, 2018

Primary Completion (Actual)

March 15, 2018

Study Completion (Estimated)

December 30, 2023

Study Registration Dates

First Submitted

March 28, 2018

First Submitted That Met QC Criteria

May 25, 2023

First Posted (Actual)

June 5, 2023

Study Record Updates

Last Update Posted (Actual)

June 5, 2023

Last Update Submitted That Met QC Criteria

May 25, 2023

Last Verified

May 1, 2023

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2018-1

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

Clinical Trials on Tuberculosis, Pulmonary

Clinical Trials on AI-based x-ray analysis and triage ("normal/tuberculosis suspected")

Search Similar Trials

Sponsors and Collaborators

Medical Conditions

Drug Interventions

CROs by country

CROs in Egypt

Conditions

Rare Diseases

Drug Interventions

Dietary Supplements

Sponsor/Collaborators

Locations

Subscribe