Chest X-ray Interpretation
Cross-source consensus on Chest X-ray Interpretation from 1 sources and 5 claims.
1 sources · 5 claims
Uses
How it works
Preparation
Risks & contraindications
Evidence quality
Highlighted claims
- Chest radiographs are a key diagnostic tool for paediatric TB, but their optimal interpretation requires specialist radiologists who are largely absent in high-burden settings. — 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
- Every CXR in the CAPTURE repository receives a consensus radiological classification from at least two expert readers who are blinded to all clinical and microbiological information. — 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
- CAPTURE radiological assessment evaluates specific features including consolidation, pleural effusion, intrathoracic lymphadenopathy, cavitation, and miliary infiltrates. — 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
- Current CAD products cannot interpret lateral CXR views, so lateral images in the CAPTURE repository are stored separately for potential future use. — 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
- Both intra-rater and inter-rater agreement among human CXR readers is poor. — 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