RecruitingNot ApplicableNCT05989802

Rapid Research in Diagnostics Development for TB Network (R2D2 Kids) and Assessing Diagnostics At POC for TB in Children (ADAPT for Kids)

Rapid Research in Diagnostics Development for Tuberculosis Network (R2D2 Kids) and Assessing Diagnostics At Point-of-care for Tuberculosis in Children (ADAPT for Kids)

Sponsor

University of California, San Francisco

Enrollment

2,100 participants

Start Date

Jan 26, 2024

Study Type

INTERVENTIONAL

Conditions

Tuberculosis Diagnostics Global Health

Summary

Every year there are an estimated 230,000 childhood deaths from TB. There is an urgent need for novel tests for TB diagnosis in children under 15 years. The Rapid Research in Diagnostics Development for TB Network (R2D2 Kids) and the Assessing Diagnostics at Point-of-care for Tuberculosis in children (ADAPT for Kids) studies seek to reduce the burden of TB worldwide by evaluating faster, simpler, and less expensive TB triage and diagnostic tests for use in children.

Eligibility

Max Age: 65 Years

Plain Language Summary

Simplified for easier understanding

This clinical trial is studying Automated Cough Sound Analysis, Automated Lung Sound Analysis, and others for people with diagnostics, global health, and other related conditions. The study is currently recruiting participants at 3 locations. People eligible for this study include up to age 65 Years.

This summary was AI-generated to explain the trial in plain language. It is not medical advice. Always discuss eligibility with your doctor before enrolling in a clinical trial.

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Interventions

DIAGNOSTIC_TESTOral swab molecular testing

Swab-based testing provides a non-invasive approach to collect respiratory specimens for TB testing. Data in adults suggests that swab-based testing could be valuable when sputum collection is not feasible or available.

DIAGNOSTIC_TESTAutomated Cough Sound Analysis

Cough sounds can be collected through a mobile phone and tablet, and then analyzed with machine learning algorithms to predict TB.

DIAGNOSTIC_TESTAutomated Lung Sound Analysis

Lung sounds can be collected with a non-invasive digital stethoscope, and then saved on a tablet or phone and analyzed by machine learning algorithms to predict TB.

OTHERChest X Ray Computer Aided Detection

Several artificial intelligence algorithms have been developed to predict TB, though this has not yet been validated in children.