Artificial intelligence in the evaluation and treatment of osteoporotic and fragility fractures: A systematic review.
Authors
Affiliations (7)
Affiliations (7)
- Department of Orthopaedics and Traumatology, The Chinese University of Hong Kong, Hong Kong, China.
- Department of Orthopaedics and Traumatology, The Chinese University of Hong Kong, Hong Kong, China. Electronic address: [email protected].
- Department of Orthopaedics and Traumatology, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China.
- School of Biological and Medical Engineering, Beihang University, Beijing, China.
- Department of Orthopaedics and Traumatology, The Chinese University of Hong Kong, Hong Kong, China; Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China.
- Department of Orthopaedics, Shenzhen Hospital of Southern Medical University, Shenzhen, China.
- Department of Orthopaedics and Traumatology, The Chinese University of Hong Kong, Hong Kong, China. Electronic address: [email protected].
Abstract
Osteoporotic and fragility fractures impose a significant global health burden, especially with the aging population. Despite advancements in imaging, risk assessment, and surgical techniques, underdiagnosis and undertreatment persists. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), offers promise for enhancing fracture risk prediction, imaging-based diagnosis, clinical decision support, and postoperative outcome monitoring. To systematically review AI applications in the evaluation and treatment of osteoporotic and fragility fractures, summarizing performance, limitations, evidence gaps, and future directions for clinical translation. A PRISMA-compliant search was conducted in PubMed, IEEE Xplore, Google Scholar, and Web of Science from inception to October 2025. Inclusion criteria targeted original English-language studies in adults using AI/ML/DL for fracture risk prediction, diagnosis, treatment planning, intraoperative guidance, or postoperative management. Data extraction focused on study design, population, AI methods, performance metrics, and validation. Of 1286 records, 21 studies were included, clustering into three domains: fracture risk prediction (n = 10) using clinical, biochemical, and imaging data, often outperforming tools like FRAX; bone mineral density (BMD) estimation and osteoporosis screening from CT, X-ray, or opportunistic imaging (n = 6); and prognosis/postoperative outcomes (n = 5). AI demonstrates robust performance in BMD estimation, fracture detection, and risk prediction, frequently surpassing traditional methods. However, methodological heterogeneity, bias risks, and limited prospective/multicenter validation hinder translation. Future efforts should prioritize transparent reporting, external validation, regulatory compliance, and user-centered integration.