ArticleScientific reports2022
Artificial intelligence used to diagnose osteoporosis from risk factors in clinical data and proposing sports protocols.
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 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk prediction models for postmenopausal osteoporosis: a systematic review and meta-analysis study.BMC musculoskeletal disorders · 2026Pooled it
- Risk factors and early onset predictors of contralateral hip fractures following initial treatment of the other hip fracture.BMC musculoskeletal disorders · 2026Article
- An intelligent auxiliary diagnostic system for early osteoporosis screening using stacking ensemble learning.BMC medical informatics and decision making · 2026Article
- Consensus statement on the application of artificial intelligence in osteoporosis screening and management: perspectives from the Asia-Pacific region.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 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
- Machine learning-based prediction model for fall risk among individuals with arthritis in China: an analysis using the China Health and Retirement Longitudinal Study (CHARLS) database.Journal of health, population, and nutrition · 2026Article
- Residual inflammatory risk as a predictor of cardiometabolic multi-morbidity: results from three prospective cohorts.Journal of health, population, and nutrition · 2026Article
- A nonlinear association and threshold effect of healthy eating index-2015 with hypertension in US children and adolescents: a cross-sectional analysis of NHANES 1999-2020.Journal of health, population, and nutrition · 2026Article
- Lumbar MRI-Based Deep Learning for Osteoporosis Prediction.Diagnostics (Basel, Switzerland) · 2026Article
- Elucidating osteoporosis response signatures in rheumatoid arthritis using explainable machine learning ensembles.BMC musculoskeletal disorders · 2026Article
- AI-assisted preoperative planning combined with robotic-assisted total knee arthroplasty vs. conventional surgery: a retrospective controlled study.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
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
- Deep learning versus manual measurement of hallux valgus angle and intermetatarsal angle on Weight-Bearing X-rays in hallux valgus.BMC musculoskeletal disorders · 2025Article
- Study on the effect and mechanism of gold nanocoated magnesium bone scaffolds for bone repair.BMC musculoskeletal disorders · 2025Article
- Deep‑learning based osteoporosis classification in knee X‑rays using transfer‑learning approach.Scientific reports · 2025Article
- Advancement in real time football analysis using fuzzy based decision-making of the WASPAS method.Scientific reports · 2025Article
- Impact of prompting on large language model performance: ChatGPT-4 performance on the 2023 hand surgery self-assessment examination.Journal of hand and microsurgery · 2025Article
- Characterizing low femoral neck BMD in Qatar Biobank participants using machine learning models.BMC musculoskeletal disorders · 2025Article
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4 authors.
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Abstract
Osteoporosis (OP) is characterized by diminished bone mass and deteriorating bone structure that increases the chance of fractures in the spine, hips, and wrists. In this paper, a novel data processing method of artificial intelligence (AI) is used for evaluating, predicting, and classifying OP risk factors in clinical data of men and women separately. Additionally, artificial intelligence was used to suggest the most appropriate sports programs for treatment. Data was obtained from dual-energy x-ray absorption scanning center of Ayatollah Kashani, Milad, and Khatam al-Anbia hospitals in Tehran, Iran. The subjects included 1224 men and women. Models were developed using decision tree, random forest (RF), k-nearest neighbor, support vector machine, gradient boosting (GB), Extra trees, Ada Boost (AB), and artificial neural network multilayer perceptron analysis to predict osteoporosis and to recommend sports programs. Data was divided into training (80%) and test dataset (20%). The results were obtained on a 20% test dataset. Area under receiver operating characteristic curve (AUROC) was used to compare the performance of the models. To predict healthy individuals, osteopenia and osteoporosis, the FR algorithm with AUROC 0.91 performed best in men and the GB algorithm with AUROC 0.95 performed best in women compared to other classification algorithms. Prediction of RF algorithm in women and men with AUROC 0.96 and 0.99, respectively, showed the highest performance in diagnosing the type of exercise for healthy individuals and those with osteopenia and OP. Eight AI algorithms were developed and compared to accurately predict osteoporosis risk factors and classify individuals into three categories: healthy, osteopenia, and OP. In addition, the AI algorithms were developed to recommend the most appropriate sports programs as part of treatment. Applying the AI algorithms in a clinical setting could help primary care providers classify patients with osteoporosis and improve treatment by recommending appropriate exercise programs.
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