Use of artificial intelligence and radiomics for diagnosis and prediction of osteoporotic fractures: a systematic review.
Authors
Affiliations (2)
Affiliations (2)
- Department of Orthopaedic Surgery, College of Medicine and Life Sciences, The University of Toledo, Toledo, OH 43614, USA. Electronic address: [email protected].
- Department of Orthopaedic Surgery, College of Medicine and Life Sciences, The University of Toledo, Toledo, OH 43614, USA.
Abstract
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. 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. 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<sup>2</sup>) values ≥0.800. 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.