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

Study Overview
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Brief Summary
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Testing of AI solutions to assess diagnostic accuracy for tuberculosis detection.

Detailed Description
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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.

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

Conditions
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Tuberculosis, Pulmonary

Intervention / Treatment
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* Diagnostic Test: AI-based x-ray analysis and triage (normal/tuberculosis suspected)


Participation Criteria
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Eligibility Criteria
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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)

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 80 Years

Sexes Eligible for Study
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All

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Sample N1<br>(n=140) with ratio normal: tuberculosis 50:50 | Diagnostic Test: AI-based x-ray analysis and triage (normal/tuberculosis suspected)<br>* 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).<br>* Other names: artificial intelligence analysis of medical images;|
| Sample N2<br>(n=150) with ratio normal: tuberculosis 95:5 | Diagnostic Test: AI-based x-ray analysis and triage (normal/tuberculosis suspected)<br>* 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).<br>* Other names: artificial intelligence analysis of medical images;|

What is the study measuring?
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Primary Outcome Measures

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

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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artificial intelligence, AI, deep learning, tuberculosis, radiology, x-ray, e-health, screening, chest