PDF(Pubmed)
Abstract：
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.
摘要：
骨质疏松性骨折由于其独特的骨代谢特征给糖尿病患者带来了巨大的负担。限制了常规裂缝预测工具的功效。人工智能（AI）算法在预测骨质疏松性骨折方面显示出巨大的前景。这篇综述旨在评估传统裂缝预测工具(FRAX，Q断裂,和GarvanFRC）在糖尿病和骨质疏松症患者中，回顾基于人工智能的裂缝预测成果，并评估AI算法在该人群中的潜在效率。这项全面的文献检索是在Pubmed和WebofScience中进行的。我们发现，由于糖尿病和骨质疏松症患者的骨代谢特征不同，常规预测工具在预测骨折方面的准确性有限。相反，AI算法在提高预测精度和改善患者预后方面显示出巨大潜力。然而,利用人工智能算法预测糖尿病患者骨质疏松性骨折仍处于起步阶段，需要进一步的研究来验证其疗效并评估其在临床实践中应用的潜在优势.
