ArticleScientific reports2023
Prediction of osteoporosis in patients with rheumatoid arthritis using machine learning.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 24 citations in OpenAlex.
- An interpretable machine learning model integrating ultrasound and clinical variables for predicting osteoporosis in patients with rheumatoid arthritis.Clinical rheumatology · 2026Article
- Artificial Intelligence-Assisted Structural Analysis of Bones with Paget's Disease of Bone and Osteoporosis: Lessons from Mouse Models.Diagnostics (Basel, Switzerland) · 2026Article
- An explainable machine learning model for predicting one-year osteoporosis risk: development and validation in a prospective cohort.BMC musculoskeletal disorders · 2026Article
- Osteoporosis in rheumatoid arthritis: a new perspective on risk factors and clinical prediction models.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026Review
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- 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
- 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
- Article
- Prevalence and Predictors of Osteoporosis Among Saudi Patients with Rheumatoid Arthritis: A Single-Center Cross-Sectional Study.Open access rheumatology : research and reviews · 2026Article
- Observational
- Development and validation of an interpretable machine learning model for osteoporosis prediction using routine blood tests: a retrospective cohort study.BMC medical informatics and decision making · 2025Article
- A machine learning tool for predicting newly diagnosed osteoporosis in primary healthcare in the Stockholm Region.Scientific reports · 2025Article
- Article
- Machine learning models to predict osteoporosis in patients with chronic kidney disease stage 3-5 and end-stage kidney disease.Scientific reports · 2025Article
- A cross-sectional study comparing machine learning and logistic regression techniques for predicting osteoporosis in a group at high risk of cardiovascular disease among old adults.BMC geriatrics · 2025Article
- Construction of a clinical prediction model for osteoporosis in asymptomatic elderly population based on machine learning algorithm.Frontiers in medicine · 2025Article
- Current application, possibilities, and challenges of artificial intelligence in the management of rheumatoid arthritis, axial spondyloarthritis, and psoriatic arthritis.Therapeutic advances in musculoskeletal disease · 2025Review
- What does artificial intelligence mean in rheumatology?Archives of rheumatology · 2024Review
- Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management.Frontiers in immunology · 2024Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Osteoporosis is a serious health concern in patients with rheumatoid arthritis (RA). Machine learning (ML) models have been increasingly incorporated into various clinical practices, including disease classification, risk prediction, and treatment response. However, only a few studies have focused on predicting osteoporosis using ML in patients with RA. We aimed to develop an ML model to predict osteoporosis using a representative Korean RA cohort database. The KORean Observational study Network for Arthritis (KORONA) database, established by the Clinical Research Center for RA in Korea, was used in this study. Among the 5077 patients registered in KORONA, 2374 patients were included in this study. Four representative ML algorithms were used for the prediction: logistic regression (LR), random forest, XGBoost (XGB), and LightGBM. The accuracy, F1 score, and area under the curve (AUC) of each model were measured. The LR model achieved the highest AUC value at 0.750, while the XGB model achieved the highest accuracy at 0.682. Body mass index, age, menopause, waist and hip circumferences, RA surgery, and monthly income were risk factors of osteoporosis. In conclusion, ML algorithms are a useful option for screening for osteoporosis in patients with RA.
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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.