ArticleOsteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA2021
Comparing three machine learning approaches to design a risk assessment tool for future fractures: predicting a subsequent major osteoporotic fracture in fracture patients with osteopenia and osteoporosis.
Article in Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 4 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 4 syntheses or guidelines pooled it, 62 citations in OpenAlex.
- Artificial Intelligence Approaches for Osteoporotic Fracture Risk Prediction Using Administrative Health Data: A Systematic Review.Calcified tissue international · 2026Pooled it
- Performance of clinical prediction models for identifying postmenopausal osteoporosis: a systematic review and meta-analysis.BMC musculoskeletal disorders · 2026Pooled 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
- An explainable machine learning approach to predict fragility fractures and the identification of important features.Scientific reports · 2026Article
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
- Accuracy of artificial intelligence in prediction of osteoporotic fractures in comparison with dual-energy X-ray absorptiometry and the Fracture Risk Assessment Tool: A systematic review.World journal of orthopedics · 2025Article
- Basketball's Improvement in Bone Mineral Density Compared to Other Sports or Free Exercise Practice in Children and Adolescents: A Systematic Review and Meta-Analysis.Children (Basel, Switzerland) · 2025Review
- 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
- Prediction of subsequent fragility fractures: application of machine learning.BMC musculoskeletal disorders · 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
- Osteoporosis Feature Selection and Risk Prediction Model by Machine Learning Using a Cross-Sectional Database.Journal of bone metabolism · 2023Article
- Effect of Artificial Intelligence or Machine Learning on Prediction of Hip Fracture Risk: Systematic Review.Journal of bone metabolism · 2023Article
- Automated Opportunistic Trabecular Volumetric Bone Mineral Density Extraction Outperforms Manual Measurements for the Prediction of Vertebral Fractures in Routine CT.Diagnostics (Basel, Switzerland) · 2023Article
- Study on the prediction model of atherosclerotic cardiovascular disease in the rural Xinjiang population based on survival analysis.BMC public health · 2023Article
- Interpretable Deep-Learning Approaches for Osteoporosis Risk Screening and Individualized Feature Analysis Using Large Population-Based Data: Model Development and Performance Evaluation.Journal of medical Internet research · 2023Article
- Energy intake restriction significantly improves POCD after internal fixation of tibial fractures in mice.American journal of translational research · 2023Article
- Development of a Spine X-Ray-Based Fracture Prediction Model Using a Deep Learning Algorithm.Endocrinology and metabolism (Seoul, Korea) · 2022Article
- Prediction Models for Osteoporotic Fractures Risk: A Systematic Review and Critical Appraisal.Aging and disease · 2022Review
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
Four machine learning models were developed and compared to predict the risk of a future major osteoporotic fracture (MOF), defined as hip, wrist, spine and humerus fractures, in patients with a prior fracture. We developed a user-friendly tool for risk calculation of subsequent MOF in osteopenia patients, using the best performing model.
introductionMajor osteoporotic fractures (MOFs), defined as hip, wrist, spine and humerus fractures, can have serious consequences regarding morbidity and mortality. Machine learning provides new opportunities for fracture prediction and may aid in targeting preventive interventions to patients at risk of MOF. The primary objective is to develop and compare several models, capable of predicting the risk of MOF as a function of time in patients seen at the fracture and osteoporosis outpatient clinic (FO-clinic) after sustaining a fracture.
methodsPatients aged > 50 years visiting an FO-clinic were included in this retrospective study. We compared discriminative ability (concordance index) for predicting the risk on MOF with a Cox regression, random survival forests (RSF) and an artificial neural network (ANN)-DeepSurv model. Missing data was imputed using multiple imputations by chained equations (MICE) or RSF's imputation function. Analyses were performed for the total cohort and a subset of osteopenia patients without vertebral fracture.
resultsA total of 7578 patients were included, 805 (11%) patients sustained a subsequent MOF. The highest concordance-index in the total dataset was 0.697 (0.664-0.730) for Cox regression; no significant difference was determined between the models. In the osteopenia subset, Cox regression outperformed RSF (p = 0.043 and p = 0.023) and ANN-DeepSurv (p = 0.043) with a c-index of 0.625 (0.562-0.689). Cox regression was used to develop a MOF risk calculator on this subset.
conclusionWe show that predicting the risk of MOF in patients who already sustained a fracture can be done with adequate discriminative performance. We developed a user-friendly tool for risk calculation of subsequent MOF in patients with osteopenia.
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