AI-based System for Lung Tuberculosis Screening: Diagnostic Accuracy Evaluation
- Conditions
- Tuberculosis, Pulmonary
- Registration Number
- NCT05889364
- Lead Sponsor
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
- Brief Summary
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.
Recruitment & Eligibility
- Status
- ACTIVE_NOT_RECRUITING
- Sex
- All
- Target Recruitment
- 308
- no pathology in a lung on chest x-ray
- signs of lung tuberculosis on chest x-ray
- any pathology in the lungs (except tuberculosis)
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Diagnostic accuracy metric 5 Day 5 upon receipt of data Likelihood ratio
Diagnostic accuracy metric 1 Day 1 upon receipt of data Sensitivity
Diagnostic accuracy metric 2 Day 2 upon receipt of data Specificity
Diagnostic accuracy metric 6 Day 6 upon receipt of data Area under the ROC curve
Diagnostic accuracy metric 3 Day 3 upon receipt of data Positive predictor values
Diagnostic accuracy metric 4 Day 4 upon receipt of data Negative predictor values
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
Research and Practical Center of Medical Radiology, Department of Health Care of Moscow
🇷🇺Moscow, Russian Federation
Research and Practical Center of Medical Radiology, Department of Health Care of Moscow🇷🇺Moscow, Russian Federation