AI algorithms for accurate prediction of osteoporotic fractures in patients with diabetes: an up-to-date review

• Published in: Journal of orthopaedic surgery and research Vol. 18; no. 1; pp. 956 - 11
• Main Authors: Li, Zeting, Zhao, Wen, Lin, Xiahong, Li, Fangping
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 12.12.2023; BioMed Central; BMC
• Subjects: Algorithms; Artificial Intelligence; Bone Density; Bone turnover; Care and treatment; Chronic obstructive pulmonary disease; Denosumab; Diabetes; Diabetes Mellitus; Diabetics; Fracture prediction; Fractures; Hip Fractures - epidemiology; Hip joint; Humans; Kidney diseases; Medical research; Medicine, Experimental; Metabolism; Mortality; Orthopedics; Osteoporosis; Osteoporosis - complications; Osteoporosis - diagnosis; Osteoporotic fracture; Osteoporotic Fractures - diagnosis; Osteoporotic Fractures - epidemiology; Osteoporotic Fractures - etiology; Predictions; Review; Rheumatoid arthritis; Risk Assessment; Risk Factors; Type 2 diabetes; United Kingdom > UK; China; Diabetes; Fracture prediction; Artificial intelligence; Osteoporotic fracture
• ISSN: 1749-799X; 1749-799X
• DOI: 10.1186/s13018-023-04446-5

Osteoporotic fractures impose a substantial burden on patients with diabetes due to their unique characteristics in bone metabolism, limiting the efficacy of conventional fracture prediction tools. Artificial intelligence (AI) algorithms have shown great promise in predicting osteoporotic fractures. This review aims to evaluate the application of traditional fracture prediction tools (FRAX, QFracture, and Garvan FRC) in patients with diabetes and osteoporosis, review AI-based fracture prediction achievements, and assess the potential efficiency of AI algorithms in this population. This comprehensive literature search was conducted in Pubmed and Web of Science. We found that conventional prediction tools exhibit limited accuracy in predicting fractures in patients with diabetes and osteoporosis due to their distinct bone metabolism characteristics. Conversely, AI algorithms show remarkable potential in enhancing predictive precision and improving patient outcomes. However, the utilization of AI algorithms for predicting osteoporotic fractures in diabetic patients is still in its nascent phase, further research is required to validate their efficacy and assess the potential advantages of their application in clinical practice.