ArticleChildren (Basel, Switzerland)2021
Diagnostic Accuracy of 3D Ultrasound and Artificial Intelligence for Detection of Pediatric Wrist Injuries.
Article in Children (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Diagnostic accuracy of ultrasound versus X-ray for distal forearm fractures in children and adolescents: a systematic review and meta-analysis.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2024Pooled it
- Reliability-Aware Semi-supervised Mutual Learning for Acute Ischemic Stroke Lesion Segmentation.Journal of imaging informatics in medicine · 2026Article
- Pediatric fracture classification in plain radiographs using EfficientNetV2 with proximal policy optimization fine-tuning.Scientific reports · 2026Article
- Diagnostic performance of artificial intelligence in fracture detection with ultrasound: A systematic review and meta-analysis.The ultrasound journal · 2026Article
- Applications of Artificial Intelligence in Pediatric Orthopaedics.Indian journal of orthopaedics · 2026Review
- Advances in Artificial Intelligence for Wrist Joint Injury Diagnosis.International journal of medical sciences · 2026Review
- Ultrasonography can be more informative than computed tomography for diagnosing radial head fractures: An illustrative case series.Ultraschall in der Medizin (Stuttgart, Germany : 1980) · 2025Article
- Wrist and elbow fracture detection and segmentation by artificial intelligence using point-of-care ultrasound.Journal of ultrasound · 2025Article
- Advanced feature fusion of radiomics and deep learning for accurate detection of wrist fractures on X-ray images.BMC musculoskeletal disorders · 2025Article
- Anatomical site variations in healing of pediatric femoral shaft fractures: ultrasound evaluation of conservative treatment.Frontiers in pediatrics · 2025Article
- Accuracy of radiologists and radiology residents in detection of paediatric appendicular fractures with and without artificial intelligence.BMJ health & care informatics · 2024Article
- The emerging application of ultrasound technology in pediatric bone fractures: Clinical application, related issues and development prospect.Pediatric discovery · 2024Review
- Developing an artificial intelligence diagnostic tool for paediatric distal radius fractures, a proof of concept study.Annals of the Royal College of Surgeons of England · 2023Article
- 2D/3D ultrasound diagnosis of pediatric distal radius fractures by human readers vs artificial intelligence.Scientific reports · 2023Article
- Advances in Pediatric Fracture Diagnosis and Treatment Are Numerous but Great Challenges Remain.Children (Basel, Switzerland) · 2022Article
- Artificial intelligence for radiological paediatric fracture assessment: a systematic review.Insights into imaging · 2022Article
- Pediatric Fractures Are Challenging from Head to Toe.Children (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Wrist trauma is common in children, typically requiring radiography for diagnosis and treatment planning. However, many children do not have fractures and are unnecessarily exposed to radiation. Ultrasound performed at bedside could detect fractures prior to radiography. Modern tools including three-dimensional ultrasound (3DUS) and artificial intelligence (AI) have not yet been applied to this task. Our purpose was to assess (1) feasibility, reliability, and accuracy of 3DUS for detection of pediatric wrist fractures, and (2) accuracy of automated fracture detection via AI from 3DUS sweeps. Children presenting to an emergency department with unilateral upper extremity injury to the wrist region were scanned on both the affected and unaffected limb. Radiographs of the symptomatic limb were obtained for comparison. Ultrasound scans were read by three individuals to determine reliability. An AI network was trained and compared against the human readers. Thirty participants were enrolled, resulting in scans from fifty-five wrists. Readers had a combined sensitivity of 1.00 and specificity of 0.90 for fractures. AI interpretation was indistinguishable from human interpretation, with all fractures detected in the test set of 36 images (sensitivity = 1.0). The high sensitivity of 3D ultrasound and automated AI ultrasound interpretation suggests that ultrasound could potentially rule out fractures in the emergency department.
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Registered trials
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.