ArticlePediatric radiology2023
Comparison of diagnostic performance of a deep learning algorithm, emergency physicians, junior radiologists and senior radiologists in the detection of appendicular fractures in children.
Article in Pediatric radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 6 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 6 syntheses or guidelines pooled it, 36 citations in OpenAlex.
- Convolutional neural networks in paediatric fracture detection: pooled evidence from a systematic review and meta-analysis.European radiology · 2026Pooled it
- Identifying the diagnostic utility of artificial intelligence for elbow effusion detection: A systematic review and meta-analysis.Emergency radiology · 2026Pooled it
- A Systematic Review of the Clinical Impact of Implementing Artificial Intelligence in Upper Aerodigestive Tract Endoscopy.Head & neck · 2025Pooled it
- Artificial intelligence (AI) in radiological paediatric fracture assessment: an updated systematic review.European radiology · 2025Pooled it
- Impact of deep learning on pediatric elbow fracture detection: a systematic review and meta-analysis.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2025Pooled it
- Artificial intelligence in commercial fracture detection products: a systematic review and meta-analysis of diagnostic test accuracy.Scientific reports · 2024Pooled 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
- Article
- Developing an artificial intelligence tool for detecting fractures of child abuse: preliminary findings.European radiology · 2026Article
- Multivendor comparison study of artificial intelligence software for automated fracture detection in paediatric patients.Pediatric radiology · 2026Article
- Deep Learning-Based Multi-Class Pediatric Wrist Fracture Subtype Classification: A Pilot Study Comparing Convolutional Neural Network Architectures.Journal of imaging · 2026Article
- Pediatric fracture classification in plain radiographs using EfficientNetV2 with proximal policy optimization fine-tuning.Scientific reports · 2026Article
- Consensus-Level and Cluster-Adjusted Evaluation of a Large Language Model for Diagnostic Extraction from Musculoskeletal Radiology Reports.Diagnostics (Basel, Switzerland) · 2026Article
- AI-Assisted Fracture Detection in Orthopedic and Trauma Imaging: Where It Works, Where It Fails, and Principles for Safe Clinical Deployment.Diagnostics (Basel, Switzerland) · 2026Review
- Integrated Implementation Strategies to Promote the Use of AI-Assisted Diagnostic Software for Lung Nodule Screening in China: Process Evaluation Based on the RE-AIM Framework.JMIR formative research · 2026Article
- Automated AI fracture detection in initial presentation pediatric wrist X-rays: effects and benefits of adding follow-up examinations.La Radiologia medica · 2026Article
- Evaluating the diagnostic performance of artificial intelligence-assisted decision-making software for pulmonary nodules in a resource-limited setting.Journal of clinical imaging science · 2026Article
- Impact of test set composition on AI performance in pediatric wrist fracture detection in X-rays.European radiology · 2025Article
- Real-life benefit of artificial intelligence-based fracture detection in a pediatric emergency department.European radiology · 2025Article
- Artificial Intelligence in Paediatric and Adolescent Fracture Detection: A Systematic Review and Meta-Analysis.Cureus · 2025Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAdvances have been made in the use of artificial intelligence (AI) in the field of diagnostic imaging, particularly in the detection of fractures on conventional radiographs. Studies looking at the detection of fractures in the pediatric population are few. The anatomical variations and evolution according to the child's age require specific studies of this population. Failure to diagnose fractures early in children may lead to serious consequences for growth.
objectiveTo evaluate the performance of an AI algorithm based on deep neural networks toward detecting traumatic appendicular fractures in a pediatric population. To compare sensitivity, specificity, positive predictive value and negative predictive value of different readers and the AI algorithm. MATERIALS AND
methodsThis retrospective study conducted on 878 patients younger than 18 years of age evaluated conventional radiographs obtained after recent non-life-threatening trauma. All radiographs of the shoulder, arm, elbow, forearm, wrist, hand, leg, knee, ankle and foot were evaluated. The diagnostic performance of a consensus of radiology experts in pediatric imaging (reference standard) was compared with those of pediatric radiologists, emergency physicians, senior residents and junior residents. The predictions made by the AI algorithm and the annotations made by the different physicians were compared.
resultsThe algorithm predicted 174 fractures out of 182, corresponding to a sensitivity of 95.6%, a specificity of 91.64% and a negative predictive value of 98.76%. The AI predictions were close to that of pediatric radiologists (sensitivity 98.35%) and that of senior residents (95.05%) and were above those of emergency physicians (81.87%) and junior residents (90.1%). The algorithm identified 3 (1.6%) fractures not initially seen by pediatric radiologists.
conclusionThis study suggests that deep learning algorithms can be useful in improving the detection of fractures in children.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.