ArticleYonsei medical journal2013
Osteoporosis risk prediction for bone mineral density assessment of postmenopausal women using machine learning.
Article in Yonsei medical journal, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
48 citing papers in PubMed, 2 syntheses or guidelines pooled it, 97 citations in OpenAlex.
- Performance of clinical prediction models for identifying postmenopausal osteoporosis: a systematic review and meta-analysis.BMC musculoskeletal disorders · 2026Pooled it
- Two-decade dialogue between artificial intelligence and osteoporosis: research trajectories and frontier projections under bibliometric and visual analysis.Frontiers in medicine · 2025Pooled it
- Comparison of dual-energy X-ray absorptiometry and lumbar radiographs using deep learning for osteoporosis diagnosis.Journal of bone and mineral metabolism · 2026Article
- Estrogen deficiency as a common driver and integrative mechanism in osteoporosis‑osteoarthritis comorbidity (Review).Molecular medicine reports · 2026Review
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
- Obesity and bone health: reconciling the density-fragility paradox.Frontiers in endocrinology · 2026Review
- Knowledge mapping of osteoporotic fractures: a bibliometric analysis from 2014 to 2024.Archives of osteoporosis · 2025Review
- A machine learning tool for predicting newly diagnosed osteoporosis in primary healthcare in the Stockholm Region.Scientific reports · 2025Article
- Dynamic fall risk prediction in hospitalized cancer patients: development and validation of a machine learning model using multidimensional clinical data to overcome over-sensitivity in traditional scales.BMC medical informatics and decision making · 2025Article
- Interpretable machine learning model for low bone density screening in older adults using demographic and anthropometric data: findings from 2005 to 2020 NHANES.BMC medical informatics and decision making · 2025Article
- Synthetic Data-Enhanced Classification of Prevalent Osteoporotic Fractures Using Dual-Energy X-Ray Absorptiometry-Based Geometric and Material Parameters.Endocrinology and metabolism (Seoul, Korea) · 2025Article
- Comparing machine learning models for osteoporosis prediction in Tibetan middle aged and elderly women.Scientific reports · 2025Article
- Retinal vein occlusion risk prediction without fundus examination using a no-code machine learning tool for tabular data: a nationwide cross-sectional study from South Korea.BMC medical informatics and decision making · 2025Article
- A simple and user-friendly machine learning model to detect osteoporosis in health examination populations in Southern Taiwan.Bone reports · 2025Article
- Automated and code-free development of a risk calculator using ChatGPT-4 for predicting diabetic retinopathy and macular edema without retinal imaging.International journal of retina and vitreous · 2025Article
- Identification and validation of ubiquitination-associated genes of senile osteoporosis based on bioinformatics analysis.Frontiers in immunology · 2025Article
- Application of ChatGPT-4 to oculomics: a cost-effective osteoporosis risk assessment to enhance management as a proof-of-principles model in 3PM.The EPMA journal · 2024Article
- LLM-driven multimodal target volume contouring in radiation oncology.Nature communications · 2024Article
- Preventive machine learning models incorporating health checkup data and hair mineral analysis for low bone mass identification.Scientific reports · 2024Article
- Osteoporosis Prediction Using Machine-Learned Optical Bone Densitometry Data.Annals of biomedical engineering · 2024Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeA number of clinical decision tools for osteoporosis risk assessment have been developed to select postmenopausal women for the measurement of bone mineral density. We developed and validated machine learning models with the aim of more accurately identifying the risk of osteoporosis in postmenopausal women compared to the ability of conventional clinical decision tools. MATERIALS AND
methodsWe collected medical records from Korean postmenopausal women based on the Korea National Health and Nutrition Examination Surveys. The training data set was used to construct models based on popular machine learning algorithms such as support vector machines (SVM), random forests, artificial neural networks (ANN), and logistic regression (LR) based on simple surveys. The machine learning models were compared to four conventional clinical decision tools: osteoporosis self-assessment tool (OST), osteoporosis risk assessment instrument (ORAI), simple calculated osteoporosis risk estimation (SCORE), and osteoporosis index of risk (OSIRIS).
resultsSVM had significantly better area under the curve (AUC) of the receiver operating characteristic than ANN, LR, OST, ORAI, SCORE, and OSIRIS for the training set. SVM predicted osteoporosis risk with an AUC of 0.827, accuracy of 76.7%, sensitivity of 77.8%, and specificity of 76.0% at total hip, femoral neck, or lumbar spine for the testing set. The significant factors selected by SVM were age, height, weight, body mass index, duration of menopause, duration of breast feeding, estrogen therapy, hyperlipidemia, hypertension, osteoarthritis, and diabetes mellitus.
conclusionConsidering various predictors associated with low bone density, the machine learning methods may be effective tools for identifying postmenopausal women at high risk for 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.