Convolutional neural networks in paediatric fracture detection: pooled evidence from a systematic review and meta-analysis

Abstract

OBJECTIVE: The objective of this review was to systematically evaluate the diagnostic accuracy of artificial intelligence (AI) models for detecting paediatric appendicular fractures on plain radiographs. MATERIALS AND METHODS: ), and negative likelihood ratio (LR⁻). The risk of bias was assessed using QUADAS-2. Random-effects models and hierarchical summary receiver operating characteristic (HSROC) curves were applied. RESULTS: was 9.32, and LR⁻ was 0.089. Most studies had a low risk of bias, though many were retrospective and single-centre with limited external validation. CONCLUSION: AI models, particularly deep learning architectures, demonstrate high diagnostic accuracy for detecting paediatric appendicular fractures on radiographs, approaching expert-level performance and improving the diagnostic abilities of junior clinicians. However, broader clinical adoption requires robust external validation and prospective integration into clinical workflows. KEY POINTS: Question What is the diagnostic accuracy of artificial intelligence models for detecting paediatric appendicular fractures on plain radiographs? Findings AI models showed high diagnostic accuracy for paediatric appendicular fractures, with a pooled sensitivity of 0.92, specificity of 0.90, strong HSROC performance, and consistent results across limb subgroups. Clinical relevance AI-assisted fracture detection may improve diagnostic accuracy, support junior clinicians, and reduce delays in identifying paediatric appendicular fractures, enhancing patient safety and enabling faster, more efficient care pathways in emergency and outpatient settings.

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