ArticleInsights into imaging2022
Artificial intelligence for radiological paediatric fracture assessment: a systematic review.
Article in Insights into imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Convolutional neural networks in paediatric fracture detection: pooled evidence from a systematic review and meta-analysis.European radiology · 2026Pooled it
- Diagnostic accuracy of deep learning models for detecting wrist and distal forearm fractures in pediatric patients: A systematic review and meta-analysis.Emergency radiology · 2026Review
- Multivendor comparison study of artificial intelligence software for automated fracture detection in paediatric patients.Pediatric radiology · 2026Article
- Deep learning improves image quality in motion-robust and sedation-free pediatric brain MRI.European radiology · 2026Article
- Artificial intelligence for pediatric fracture detection: impact on diagnostic revisions and patient recall rates in a tertiary emergency setting.BMC emergency medicine · 2026Article
- Development and validation of a vision transformer model for radiographic assessment of pediatric humeroradial joint alignment.Journal of orthopaedics and traumatology : official journal of the Italian Society of Orthopaedics and Traumatology · 2026Article
- Development and validation of a deep learning model for radiographic classification of pediatric femoral neck fractures.Journal of orthopaedics and traumatology : official journal of the Italian Society of Orthopaedics and Traumatology · 2026Article
- Pediatric fracture classification in plain radiographs using EfficientNetV2 with proximal policy optimization fine-tuning.Scientific reports · 2026Article
- Child and adolescent psychiatry: challenges, solutions, opportunities, and future directions.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2026Article
- A Comprehensive X-ray Dataset for Pediatric Ulna and Radius Fractures Analysis.Scientific data · 2026Article
- Article
- External validation of an artificial intelligence tool for fracture detection in children with osteogenesis imperfecta: a multireader study.European radiology · 2026Article
- A deep learning framework for automated fracture detection and localization in radiographic images using convolutional neural networks.Frontiers in medicine · 2026Article
- Proof-of-concept comparison of an artificial intelligence-based bone age assessment tool with Greulich-Pyle and Tanner-Whitehouse version 2 methods in a pediatric cohort.Pediatric radiology · 2026Article
- Review
- Artificial Intelligence Applications in Musculoskeletal Imaging.Current reviews in musculoskeletal medicine · 2025Review
- Artificial intelligence based sonographic differentiation between skull fractures and normal sutures in young children.Scientific reports · 2025Article
- Artificial intelligence algorithms in orthopaedics: A narrative review of methods and clinical applications.Journal of experimental orthopaedics · 2025Review
- Analyzing pediatric forearm X-rays for fracture analysis using machine learning.International journal of computer assisted radiology and surgery · 2025Article
- Artificial intelligence in the interpretation of upper extremity trauma radiographs: a systematic review and meta-analysis.JSES reviews, reports, and techniques · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundMajority of research and commercial efforts have focussed on use of artificial intelligence (AI) for fracture detection in adults, despite the greater long-term clinical and medicolegal implications of missed fractures in children. The objective of this study was to assess the available literature regarding diagnostic performance of AI tools for paediatric fracture assessment on imaging, and where available, how this compares with the performance of human readers. MATERIALS AND
methodsMEDLINE, Embase and Cochrane Library databases were queried for studies published between 1 January 2011 and 2021 using terms related to 'fracture', 'artificial intelligence', 'imaging' and 'children'. Risk of bias was assessed using a modified QUADAS-2 tool. Descriptive statistics for diagnostic accuracies were collated.
resultsNine eligible articles from 362 publications were included, with most (8/9) evaluating fracture detection on radiographs, with the elbow being the most common body part. Nearly all articles used data derived from a single institution, and used deep learning methodology with only a few (2/9) performing external validation. Accuracy rates generated by AI ranged from 88.8 to 97.9%. In two of the three articles where AI performance was compared to human readers, sensitivity rates for AI were marginally higher, but this was not statistically significant.
conclusionsWide heterogeneity in the literature with limited information on algorithm performance on external datasets makes it difficult to understand how such tools may generalise to a wider paediatric population. Further research using a multicentric dataset with real-world evaluation would help to better understand the impact of these tools.
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