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

N/AUnknownNCT05889364

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department308 enrolled

Overview

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

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

308

Conditions

Tuberculosis, Pulmonary

Interventions

AI-based x-ray analysis and triage ("normal/tuberculosis suspected")DIAGNOSTIC_TEST

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).

Eligibility

Sex: ALLMin age: 18 YearsMax age: 80 Years

Medical Language ↔ Plain English

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)

Locations (1)

Research and Practical Center of Medical Radiology, Department of Health Care of Moscow

Moscow, Russia

Outcomes

Primary Outcomes

Diagnostic accuracy metric 1

Sensitivity

Time frame: Day 1 upon receipt of data

Diagnostic accuracy metric 2

Specificity

Time frame: Day 2 upon receipt of data

Diagnostic accuracy metric 3

Positive predictor values

Time frame: Day 3 upon receipt of data

Diagnostic accuracy metric 4

Negative predictor values

Time frame: Day 4 upon receipt of data

Diagnostic accuracy metric 5

Likelihood ratio

Time frame: Day 5 upon receipt of data

Diagnostic accuracy metric 6

Area under the ROC curve

Time frame: Day 6 upon receipt of data

Data from ClinicalTrials.gov

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