Use of artificial intelligence and radiomics for diagnosis and prediction of osteoporotic fractures: a systematic review
Year: 2026
Type: review
Study design: Systematic Review
Abstract: AIM Osteoporosis (OP) is a prevalent metabolic bone disease causing millions of fractures annually, leading to significant healthcare and economic burdens. Early diagnosis and intervention are critical, particularly among postmenopausal women and the elderly. Traditional bone mineral density tests, while useful, are limited in predicting fracture risk and are not always accessible. This review summarises recent advancements in applying machine learning (ML) and radiomics techniques for fracture risk prediction and OP assessment using computed tomography and magnetic resonance imaging. MATERIALS AND METHODS A comprehensive literature search was conducted in PubMed, Embase, and Web of Science using the keywords ‘radiomics', ‘osteoporosis', and ‘fracture'. A total of 119 studies published between January 15, 2021, and March 15, 2025, were identified through PubMed (31), Embase (45), and Web of Science (43). After applying predefined inclusion and exclusion criteria, 20 studies were included for analysis. These studies used various ML algorithms and radiomics approaches to extract quantitative features from imaging data. RESULTS Most studies included predominantly female participants and focussed on vertebral bones. Integrating radiomics features with clinical data improved the predictive accuracy of fracture risk models. Commonly used algorithms included logistic regression, extreme gradient boosting, support vector machines, random forests, and neural networks. Fourteen studies achieved area under the curve (AUC) or coefficient of determination (R 2 ) values ≥0.800. CONCLUSION ML and radiomics show great potential for enhancing OP and fracture risk assessment. Future studies should emphasise larger, multicentre datasets and broader clinical integration to improve model robustness and clinical applicability.
Source: Clinical Radiology
Authors: A.C. Jayasuriya, M. Skie
Institution: University of Toledo
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,530
OpenAlex ID: W7135170022