ArticleScientific reports2022
Machine learning outperforms clinical experts in classification of hip fractures.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 4 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Artificial Intelligence-Guided Assessment of Femoral Neck Fractures in Radiographs: A Systematic Review and Multilevel Meta-Analysis.Orthopaedic surgery · 2025Pooled it
- Gender differences between smoking and the risk of hip fracture.Journal of bone and mineral metabolism · 2024Pooled it
- Artificial Intelligence for Hip Fracture Detection and Outcome Prediction: A Systematic Review and Meta-analysis.JAMA network open · 2023Pooled it
- Artificial intelligence and machine learning on diagnosis and classification of hip fracture: systematic review.Journal of orthopaedic surgery and research · 2022Pooled it
- Trochanteric region fracture classification network (TRFC-net): a deep learning solution to increasing inexperienced residents' trochanteric region fracture classification accuracy on X-rays.Quantitative imaging in medicine and surgery · 2026Article
- Deriving the OTA/AO fracture classification from routinely collected radiology reports using a large language model.OTA international : the open access journal of orthopaedic trauma · 2026Article
- Classification and segmentation of hip fractures in x-rays: highlighting fracture regions for interpretable diagnosis.Insights into imaging · 2025Article
- Role of Artificial Intelligence in Minimizing Missed and Undiagnosed Fractures Among Trainee Residents.Journal of multidisciplinary healthcare · 2025Review
- Examining the relationships between patients' multimorbidity trajectories and prognostic outcomes after the initial hip fracture.Scientific reports · 2024Article
- Development and reporting of artificial intelligence in osteoporosis management.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2024Review
- A staged approach using machine learning and uncertainty quantification to predict the risk of hip fracture.Bone reports · 2024Article
- A Staged Approach using Machine Learning and Uncertainty Quantification to Predict the Risk of Hip Fracture.ArXiv · 2024Article
- Proximal femur fracture detection on plain radiography via feature pyramid networks.Scientific reports · 2024Article
- A deep learning approach using an ensemble model to autocreate an image-based hip fracture registry.OTA international : the open access journal of orthopaedic trauma · 2023Article
- Article
- Detection of incomplete atypical femoral fracture on anteroposterior radiographs via explainable artificial intelligence.Scientific reports · 2023Article
- Deep Learning for Fully Automated Radiographic Measurements of the Pelvis and Hip.Diagnostics (Basel, Switzerland) · 2023Article
- A review on artificial intelligence for the diagnosis of fractures in facial trauma imaging.Frontiers in artificial intelligence · 2023Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hip fractures are a major cause of morbidity and mortality in the elderly, and incur high health and social care costs. Given projected population ageing, the number of incident hip fractures is predicted to increase globally. As fracture classification strongly determines the chosen surgical treatment, differences in fracture classification influence patient outcomes and treatment costs. We aimed to create a machine learning method for identifying and classifying hip fractures, and to compare its performance to experienced human observers. We used 3659 hip radiographs, classified by at least two expert clinicians. The machine learning method was able to classify hip fractures with 19% greater accuracy than humans, achieving overall accuracy of 92%.
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