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

Sponsor
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department (Other)
Overall Status
Active, not recruiting
CT.gov ID
NCT05889364
Collaborator
(none)
308
Enrollment
1
Location
70.9
Anticipated Duration (Months)
4.3
Patients Per Site Per Month

Study Details

Study Description

Brief Summary

Testing of AI solutions to assess diagnostic accuracy for tuberculosis detection.

Condition or Disease Intervention/Treatment Phase
  • Diagnostic Test: AI-based x-ray analysis and triage ("normal/tuberculosis suspected")

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

Study Type:
Observational
Actual Enrollment :
308 participants
Observational Model:
Other
Time Perspective:
Retrospective
Official Title:
AI-based System for Lung Tuberculosis Screening: Diagnostic Accuracy Evaluation
Actual Study Start Date :
Feb 1, 2018
Actual Primary Completion Date :
Mar 15, 2018
Anticipated Study Completion Date :
Dec 30, 2023

Arms and Interventions

Arm 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

    		•

    Outcome Measures

    Primary Outcome Measures

    1. Diagnostic accuracy metric 1 [Day 1 upon receipt of data]

      Sensitivity

    2. Diagnostic accuracy metric 2 [Day 2 upon receipt of data]

      Specificity

    3. Diagnostic accuracy metric 3 [Day 3 upon receipt of data]

      Positive predictor values

    4. Diagnostic accuracy metric 4 [Day 4 upon receipt of data]

      Negative predictor values

    5. Diagnostic accuracy metric 5 [Day 5 upon receipt of data]

      Likelihood ratio

    6. Diagnostic accuracy metric 6 [Day 6 upon receipt of data]

      Area under the ROC curve

    Eligibility Criteria

    Criteria

    Ages Eligible for Study:
    18 Years to 80 Years
    Sexes Eligible for Study:
    All
    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)

    Contacts and Locations

    Locations

    Site City State Country Postal Code
    1 Research and Practical Center of Medical Radiology, Department of Health Care of Moscow Moscow Russian Federation 109029

    Sponsors and Collaborators

    • 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 Documents (Full-Text)

    None provided.

    More Information

    Publications

    None provided.
    Responsible Party:
    Anton V. Vladzymyrskyy, Deputy CEO, Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
    ClinicalTrials.gov Identifier:
    NCT05889364
    Other Study ID Numbers:
    • 2018-1
    First Posted:
    Jun 5, 2023
    Last Update Posted:
    Jun 5, 2023
    Last Verified:
    May 1, 2023
    Individual Participant Data (IPD) Sharing Statement:
    No
    Plan to Share IPD:
    No
    Studies a U.S. FDA-regulated Drug Product:
    No
    Studies a U.S. FDA-regulated Device Product:
    No
    Keywords provided by Anton V. Vladzymyrskyy, Deputy CEO, Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
    AI
    deep learning
    tuberculosis
    radiology
    x-ray
    e-health
    screening
    chest
    artificial intelligence
    Additional relevant MeSH terms:
    Tuberculosis
    Tuberculosis, Pulmonary

    Study Results

    No Results Posted as of Jun 5, 2023