SynthesisBMJ open2023
Predictive value of machine learning on fracture risk in osteoporosis: a systematic review and meta-analysis.
Synthesis in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 4 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Artificial Intelligence Approaches for Osteoporotic Fracture Risk Prediction Using Administrative Health Data: A Systematic Review.Calcified tissue international · 2026Pooled it
- Machine learning-based risk predictive model for postoperative refractures in patients with osteoporotic vertebral compression fractures: a systematic review and critical appraisal.BMC musculoskeletal disorders · 2026Pooled it
- Diagnostic Performance of Deep Learning and Radiomics in Extracranial Carotid Plaque Detection: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Risk prediction models for postmenopausal osteoporosis: a systematic review and meta-analysis study.BMC musculoskeletal disorders · 2026Pooled it
- Precision management of gastrointestinal tumor-associated osteoporosis driven by cutting-edge technologies: Current status, challenges, and future prospects.World journal of methodology · 2026Review
- In silico augmentation strategies for enhanced machine learning performance in fracture recognition.Scientific reports · 2026Article
- SuperLearner approach for predicting imminent risk of fracture in older Chinese patients with newly diagnosed osteoporosis based on their routine blood test markers.BMC musculoskeletal disorders · 2026Article
- Application of machine learning in osteoporosis screening: a narrative review.NPJ digital medicine · 2026Review
- Artificial intelligence and robotic technologies redefining precision and personalization in orthopedic surgery: a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
- Identification of key predictors of postmenopausal osteoporosis from routine clinical indicators using explainable machine learning.PloS one · 2026Article
- Obesity and bone health: reconciling the density-fragility paradox.Frontiers in endocrinology · 2026Review
- Explainable machine learning for osteoporosis detection in patients with osteopenia: model development and validation using routine clinical data from an Asian cohort.Frontiers in endocrinology · 2026Article
- Artificial Intelligence and Machine Learning in the Diagnosis and Management of Osteoporosis: A Comprehensive Review.Medicina (Kaunas, Lithuania) · 2025Review
- Bridging the gap: enhancing osteoporosis management through AI-driven predictive models.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025Article
- Effect of miR-654-3p targeting EMP1 on osteoblast activity and differentiation in delayed fracture healing.Journal of orthopaedic surgery and research · 2025Article
- Optimizing patient outcomes post-glucocorticoid therapy: a call for enhanced monitoring and preventive care.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025Article
- Diagnosis Osteoporosis Risk: Using Machine Learning Algorithms Among Fasa Adults Cohort Study (FACS).Endocrinology, diabetes & metabolism · 2025Article
- Article
- From Genomics to Metabolomics: Molecular Insights into Osteoporosis for Enhanced Diagnostic and Therapeutic Approaches.Biomedicines · 2024Review
- Prediction of Osteoporotic Hip Fracture Outcome: Comparative Accuracy of 27 Immune-Inflammatory-Metabolic Markers and Related Conceptual Issues.Journal of clinical medicine · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesEarly identification of fracture risk in patients with osteoporosis is essential. Machine learning (ML) has emerged as a promising technique to predict the risk, whereas its predictive performance remains controversial. Therefore, we conducted this systematic review and meta-analysis to explore the predictive efficiency of ML for the risk of fracture in patients with osteoporosis.
methodsRelevant studies were retrieved from four databases (PubMed, Embase, Cochrane Library and Web of Science) until 31 May 2023. A meta-analysis of the C-index was performed using a random-effects model, while a bivariate mixed-effects model was used for the meta-analysis of sensitivity and specificity. In addition, subgroup analysis was performed according to the types of ML models and fracture sites.
resultsFifty-three studies were included in our meta-analysis, involving 15 209 268 patients, 86 prediction models specifically developed for the osteoporosis population and 41 validation sets. The most commonly used predictors in these models encompassed age, BMI, past fracture history, bone mineral density T-score, history of falls, BMD, radiomics data, weight, height, gender and other chronic diseases. Overall, the pooled C-index of ML was 0.75 (95% CI: 0.72, 0.78) and 0.75 (95% CI: 0.71, 0.78) in the training set and validation set, respectively; the pooled sensitivity was 0.79 (95% CI: 0.72, 0.84) and 0.76 (95% CI: 0.80, 0.81) in the training set and validation set, respectively; and the pooled specificity was 0.81 (95% CI: 0.75, 0.86) and 0.83 (95% CI: 0.72, 0.90) in the training set and validation set, respectively.
conclusionsML has a favourable predictive performance for fracture risk in patients with osteoporosis. However, most current studies lack external validation. Thus, external validation is required to verify the reliability of ML models. PROSPERO REGISTRATION NUMBER: CRD42022346896.
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