ArticleBJR open2024
Improving traumatic fracture detection on radiographs with artificial intelligence support: a multi-reader study.
Article in BJR open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 4 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Enhanced fracture detection on radiographs with AI assistance for clinicians: a systematic review and meta-analysis.Annals of medicine · 2026Pooled it
- Diagnostic accuracy of AI-assisted versus independent physician interpretation for bone fractures: a systematic review and meta-analysis.European radiology experimental · 2026Pooled it
- Artificial intelligence software to help detect fractures on X-rays in urgent care: An Early Value Assessment.Health technology assessment (Winchester, England) · 2026Pooled it
- Artificial intelligence in virtual fracture clinics: a systematic review of imaging and clinical-text tools.Journal of orthopaedic surgery and research · 2026Pooled it
- Diagnostic Accuracy of Artificial Intelligence Versus Musculoskeletal Radiologists for Foot and Ankle Fracture Detection on Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Multivendor comparison study of artificial intelligence software for automated fracture detection in paediatric patients.Pediatric radiology · 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
- Artificial intelligence for detecting acute heart failure on chest CT: prospective clinical proof-of-concept validation.European radiology experimental · 2026Article
- Artificial Intelligence in Orthopaedics: Clinical Performance, Limitations, and Translational Readiness-A Review.Journal of clinical medicine · 2026Review
- Enhancing pediatric fracture diagnostics with artificial intelligence: insights and recommendations.Revista da Associacao Medica Brasileira (1992) · 2026Article
- An Overview of Artificial Intelligence Applications in Radiological Imaging for Bone Fracture Diagnosis.Cureus · 2025Review
- Real-life benefit of artificial intelligence-based fracture detection in a pediatric emergency department.European radiology · 2025Article
- Artificial intelligence algorithms in orthopaedics: A narrative review of methods and clinical applications.Journal of experimental orthopaedics · 2025Review
- Artificial intelligence-assisted analysis of musculoskeletal imaging-A narrative review of the current state of machine learning models.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Review
- Cross-validation of an artificial intelligence tool for fracture classification and localization on conventional radiography in Dutch population.Insights into imaging · 2025Article
- Global burden of pediatric fracture (1992-2021) and projections of future disease burden trends.BMC pediatrics · 2025Article
- Artificial intelligence in fracture detection on radiographs: a literature review.Japanese journal of radiology · 2025Review
- Artificial Intelligence-Empowered Radiology-Current Status and Critical Review.Diagnostics (Basel, Switzerland) · 2025Review
- Adoption, orchestration, and deployment of artificial intelligence within the National Health Service-facilitators and barriers: an expert roundtable discussion.BJR artificial intelligence · 2024Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: The aim of this study was to evaluate the diagnostic performance of nonspecialist readers with and without the use of an artificial intelligence (AI) support tool to detect traumatic fractures on radiographs of the appendicular skeleton. Methods: The design was a retrospective, fully crossed multi-reader, multi-case study on a balanced dataset of patients (≥2 years of age) with an AI tool as a diagnostic intervention. Fifteen readers assessed 340 radiographic exams, with and without the AI tool in 2 different sessions and the time spent was automatically recorded. Reference standard was established by 3 consultant radiologists. Sensitivity, specificity, and false positives per patient were calculated. Results: Patient-wise sensitivity increased from 72% to 80% ( Conclusions: The diagnostic performance for detection of traumatic fractures on radiographs of the appendicular skeleton improved among nonspecialist readers tested AI fracture detection support tool showed an overall reader improvement in sensitivity and specificity when supported by an AI tool. Improvement was seen in both sensitivity and specificity without negatively affecting the interpretation time. Advances in knowledge: The division and analysis of obvious and nonobvious fractures are novel in AI reader comparison studies like this.
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Registered trials
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