Tuberculosis, Pulmonary
Conditions
Keywords
AI, deep learning, tuberculosis, radiology, x-ray, e-health, screening, chest, artificial intelligence
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.
Interventions
DIAGNOSTIC_TESTAI-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).
Sponsors
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
Study design
Observational model
OTHER
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
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)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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 3 | Day 3 upon receipt of data | Positive predictor values |
| Diagnostic accuracy metric 4 | Day 4 upon receipt of data | Negative predictor values |
| Diagnostic accuracy metric 5 | Day 5 upon receipt of data | Likelihood ratio |
| Diagnostic accuracy metric 6 | Day 6 upon receipt of data | Area under the ROC curve |
Countries
Russia
Outcome results
None listed