ArticleInternational journal of environmental research and public health2021
Development of Machine Learning Models for Prediction of Osteoporosis from Clinical Health Examination Data.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 3 of them syntheses that pooled it.
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Who cites it
30 citing papers in PubMed, 3 syntheses or guidelines pooled it, 65 citations in OpenAlex.
- The Diagnostic Value of Image-Based Machine Learning for Osteoporosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Diagnostic accuracy of deep learning in prediction of osteoporosis: a systematic review and meta-analysis.BMC musculoskeletal disorders · 2024Pooled it
- Machine learning algorithms for diagnosis of hip bone osteoporosis: a systematic review and meta-analysis study.Biomedical engineering online · 2023Pooled it
- Association of the Dietary Index for Gut Microbiota with site-specific Osteoporosis: Insights from machine learning applied to a national population.Metabolism open · 2026Article
- Article
- Advancing diagnostic equity through artificial intelligence chest radiograph screening for osteoporosis in Asian populations.NPJ digital medicine · 2026Article
- Lumbar MRI-Based Deep Learning for Osteoporosis Prediction.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- Knowledge mapping of osteoporotic fractures: a bibliometric analysis from 2014 to 2024.Archives of osteoporosis · 2025Review
- Performance evaluation of deep learning-based osteoporosis diagnostic models with conventional chest X-ray in a clinical cohort.Journal of thoracic disease · 2025Article
- Anthropometric Measurements for Predicting Low Appendicular Lean Mass Index for the Diagnosis of Sarcopenia: A Machine Learning Model.Journal of functional morphology and kinesiology · 2025Article
- Application of machine learning algorithms in osteoporosis analysis based on cardiovascular health assessed by life's essential 8: a cross-sectional study.Journal of health, population, and nutrition · 2025Article
- Machine learning models to predict osteoporosis in patients with chronic kidney disease stage 3-5 and end-stage kidney disease.Scientific reports · 2025Article
- Development and evaluation of a machine learning model for osteoporosis risk prediction in Korean women.BMC women's health · 2025Article
- A simple and user-friendly machine learning model to detect osteoporosis in health examination populations in Southern Taiwan.Bone reports · 2025Article
- Diagnosis Osteoporosis Risk: Using Machine Learning Algorithms Among Fasa Adults Cohort Study (FACS).Endocrinology, diabetes & metabolism · 2025Article
- Machine Learning-Based Interpretable Screening for Osteoporosis in Tuberculosis Spondylitis Patients Using Blood Test Data: Development and External Validation of a Novel Web-Based Risk Calculator with Explainable Artificial Intelligence (XAI).Infection and drug resistance · 2025Article
- Application of machine learning on health examination data for predicting the decrease of bone mineral density.International journal of clinical and experimental pathology · 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
- Preventive machine learning models incorporating health checkup data and hair mineral analysis for low bone mass identification.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Osteoporosis is treatable but often overlooked in clinical practice. We aimed to construct prediction models with machine learning algorithms to serve as screening tools for osteoporosis in adults over fifty years old. Additionally, we also compared the performance of newly developed models with traditional prediction models. Data were acquired from community-dwelling participants enrolled in health checkup programs at a medical center in Taiwan. A total of 3053 men and 2929 women were included. Models were constructed for men and women separately with artificial neural network (ANN), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and logistic regression (LoR) to predict the presence of osteoporosis. Area under receiver operating characteristic curve (AUROC) was used to compare the performance of the models. We achieved AUROC of 0.837, 0.840, 0.843, 0.821, 0.827 in men, and 0.781, 0.807, 0.811, 0.767, 0.772 in women, for ANN, SVM, RF, KNN, and LoR models, respectively. The ANN, SVM, RF, and LoR models in men, and the ANN, SVM, and RF models in women performed significantly better than the traditional Osteoporosis Self-Assessment Tool for Asians (OSTA) model. We have demonstrated that machine learning algorithms improve the performance of screening for osteoporosis. By incorporating the models in clinical practice, patients could potentially benefit from earlier diagnosis and treatment of osteoporosis.
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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.