ArticleApplied bionics and biomechanics2020
Risk Assessment of Hip Fracture Based on Machine Learning.
Article in Applied bionics and biomechanics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
What it found
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The trial behind it
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Predictive value of machine learning on fracture risk in osteoporosis: a systematic review and meta-analysis.BMJ open · 2023Pooled it
- Machine learning algorithms for diagnosis of hip bone osteoporosis: a systematic review and meta-analysis study.Biomedical engineering online · 2023Pooled it
- Prophylactic fixation in elderly fractures: Preventive breakthrough or unnecessary intervention?World journal of orthopedics · 2025Review
- Bone loss in young adults with HIV following antiretroviral therapy containing tenofovir disoproxil fumarate regimen using machine learning.Frontiers in pharmacology · 2025Article
- 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
- The classification algorithms to support the management of the patient with femur fracture.BMC medical research methodology · 2024Article
- Artificial intelligence in fracture detection with different image modalities and data types: A systematic review and meta-analysis.PLOS digital health · 2024Article
- AI algorithms for accurate prediction of osteoporotic fractures in patients with diabetes: an up-to-date review.Journal of orthopaedic surgery and research · 2023Review
- Machine learning applications for the prediction of extended length of stay in geriatric hip fracture patients.World journal of orthopedics · 2023Article
- Human Resource Planning and Configuration Based on Machine Learning.Computational intelligence and neuroscience · 2022Article
- Risk Analysis of Textile Industry Foreign Investment Based on Deep Learning.Computational intelligence and neuroscience · 2022Article
- Fracture Risk of Long Bone Metastases: A Review of Current and New Decision-Making Tools for Prophylactic Surgery.Cancers · 2021Review
- Enterprise Risk Assessment Based on Machine Learning.Computational intelligence and neuroscience · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying patients with high risk of hip fracture is a great challenge in osteoporosis clinical assessment. Bone Mineral Density (BMD) measured by Dual-Energy X-Ray Absorptiometry (DXA) is the current gold standard in osteoporosis clinical assessment. However, its classification accuracy is only around 65%. In order to improve this accuracy, this paper proposes the use of Machine Learning (ML) models trained with data from a biomechanical model that simulates a sideways-fall. Machine Learning (ML) models are models able to learn and to make predictions from data. During a training process, ML models learn a function that maps inputs and outputs without previous knowledge of the problem. The main advantage of ML models is that once the mapping function is constructed, they can make predictions for complex biomechanical behaviours in real time. However, despite the increasing popularity of Machine Learning (ML) models and their wide application to many fields of medicine, their use as hip fracture predictors is still limited. This paper proposes the use of ML models to assess and predict hip fracture risk. Clinical, geometric, and biomechanical variables from the finite element simulation of a side fall are used as independent variables to train the models. Among the different tested models, Random Forest stands out, showing its capability to outperform BMD-DXA, achieving an accuracy over 87%, with specificity over 92% and sensitivity over 83%.
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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.