ArticleScientific reports2022
Predicting the risk of osteoporosis in older Vietnamese women using machine learning approaches.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 41 citations in OpenAlex.
- Article
- From Molecular Pathways to Artificial Intelligence: Advancing the Understanding and Management of Osteosarcopenia.International journal of molecular sciences · 2026Review
- A multiparameter model (OsteoSC-M3) for early detection and risk stratification of osteoporosis in women: a multicentre cohort study in China.EClinicalMedicine · 2026Article
- Article
- Advancing diagnostic equity through artificial intelligence chest radiograph screening for osteoporosis in Asian populations.NPJ digital medicine · 2026Article
- Elucidating osteoporosis response signatures in rheumatoid arthritis using explainable machine learning ensembles.BMC musculoskeletal disorders · 2026Article
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- Deep Learning-Based Assessment of the Value of Vertebral Structural Parameters in Predicting Osteoporotic Vertebral Compression Fractures on Opportunistic CT Scans.International journal of general medicine · 2026Article
- 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
- Establishing correlation between bone mineral apposition rate and physiological loading using machine learning regressor.Scientific reports · 2025Article
- Improved Osteoporosis Prediction in Breast Cancer Patients Using a Novel Semi-Foundational Model.Journal of imaging informatics in medicine · 2025Article
- Machine learning is changing osteoporosis detection: an integrative review.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025Review
- Machine learning models to predict osteoporosis in patients with chronic kidney disease stage 3-5 and end-stage kidney disease.Scientific reports · 2025Article
- Comparing machine learning models for osteoporosis prediction in Tibetan middle aged and elderly women.Scientific reports · 2025Article
- Development and evaluation of a machine learning model for osteoporosis risk prediction in Korean women.BMC women's health · 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
- Prediction of osteoporosis in patients with rheumatoid arthritis using machine learning.Scientific reports · 2023Article
- Applications of Artificial Intelligence Methods for the Prediction of Osteoporotic Fractures.Life (Basel, Switzerland) · 2023Article
- Screening for Osteoporosis from Blood Test Data in Elderly Women Using a Machine Learning Approach.Bioengineering (Basel, Switzerland) · 2023Article
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Authors and funding
11 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Osteoporosis contributes significantly to health and economic burdens worldwide. However, the development of osteoporosis-related prediction tools has been limited for lower-middle-income countries, especially Vietnam. This study aims to develop prediction models for the Vietnamese population as well as evaluate the existing tools to forecast the risk of osteoporosis and evaluate the contribution of covariates that previous studies have determined to be risk factors for osteoporosis. The prediction models were developed to predict the risk of osteoporosis using machine learning algorithms. The performance of the included prediction models was evaluated based on two scenarios; in the first one, the original test parameters were directly modeled, and in the second the original test parameters were transformed into binary covariates. The area under the receiver operating characteristic curve, the Brier score, precision, recall and F1-score were calculated to evaluate the models' performance in both scenarios. The contribution of the covariates was estimated using the Permutation Feature Importance estimation. Four models, namely, Logistic Regression, Support Vector Machine, Random Forest and Neural Network, were developed through two scenarios. During the validation phase, these four models performed competitively against the reference models, with the areas under the curve above 0.81. Age, height and weight contributed the most to the risk of osteoporosis, while the correlation of the other covariates with the outcome was minor. Machine learning algorithms have a proven advantage in predicting the risk of osteoporosis among Vietnamese women over 50 years old. Additional research is required to more deeply evaluate the performance of the models on other high-risk populations.
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