Catalysing Artificial Intelligence for Paediatric Tuberculosis Research (CAPTURE): protocol for a global multicentre study establishing a paediatric chest X-ray repository to evaluate computer-aided detection algorithms.

ABSTRACT:

Introduction

The substantial case detection gap in the field of child tuberculosis (TB) disease is largely driven by inadequate diagnostic tools and approaches. Chest radiographs (CXRs) remain a key component in the evaluation of children and young adolescents (0-15 years) with presumptive TB, aiding clinicians in making the diagnosis and discriminating children with TB from those with other diseases. Widespread use and optimal interpretation of CXR is hampered by a lack of access to well-trained specialists to interpret images. Artificial intelligence CXR interpretation software, termed computer-aided detection (CAD), is now well developed for adults, yet few products have been evaluated in children. The CXR features of child TB are different from those of adults, and as a result,

SUBMITTER: Palmer M 

PROVIDER: S-EPMC12815126 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature