Computer-aided reading of chest radiographs for paediatric tuberculosis: current status and future directions.

Computer-aided detection (CAD) systems for automated reading of chest x-rays (CXRs) have been developed and approved for tuberculosis triage in adults but not in children. However, CXR is frequently the only adjunctive tool for clinical assessment in the evaluation of paediatric tuberculosis in primary care settings, and children would benefit from CAD models that can detect their unique clinical and radiographic features. To advance CAD for childhood tuberculosis, large, diverse paediatric CXR datasets linked to standardised tuberculosis classifications are required. These datasets would be used to train and validate paediatric-specific models for tuberculosis screening, diagnosis, and severity stratification. Previous studies on CAD algorithms for reading paediatric CXRs have highlighted promising approaches, including the use of transfer learning with existing deep learning models. Including data from children in CAD models is essential to improve equity and reduce the global burden of tuberculosis disease.