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<p>Osteoporotic fractures pose a growing public health burden in aging populations with chronic comorbidities. Current fracture risk tools (e.g., FRAX and QFracture) remain limited by static assessment frameworks and inadequate racial adaptation. Artificial intelligence (AI), particularly multimodal deep learning and temporal modeling, demonstrates superior predictive accuracy in elderly patients with osteoporosis by integrating dynamic physiological, genetic, and clinical variables. This review critically evaluates the clinical applicability of existing prediction tools and synthesizes advances in AI-driven modeling, highlighting four transformative frontiers: multimodal data fusion (imaging, genomics, and real-world monitoring), temporal analysis, meta-learning for cross-population generalization, and interpretable AI for clinical transparency.</p>

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Predicting Fracture Risk in Elderly Patients with Osteoporosis: Advances in Clinical Prediction Tools and Machine Learning Techniques

  • 期刊论文

  • 发表日期：

    2026年5月16日

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  • Xinxin Zhang,
  • Rui Yang,
  • Yanli Huang,
  • Hongliang Fu,
  • Lei Jin,
  • Ting Chen,
  • Pei Niu,
  • Yunlong Cheng,
  • Wanting Fu,
  • Yue Yuan,
  • Yunzhang Cheng,
  • Jian Zhang,
  • Ajing Xu

摘要

Osteoporotic fractures pose a growing public health burden in aging populations with chronic comorbidities. Current fracture risk tools (e.g., FRAX and QFracture) remain limited by static assessment frameworks and inadequate racial adaptation. Artificial intelligence (AI), particularly multimodal deep learning and temporal modeling, demonstrates superior predictive accuracy in elderly patients with osteoporosis by integrating dynamic physiological, genetic, and clinical variables. This review critically evaluates the clinical applicability of existing prediction tools and synthesizes advances in AI-driven modeling, highlighting four transformative frontiers: multimodal data fusion (imaging, genomics, and real-world monitoring), temporal analysis, meta-learning for cross-population generalization, and interpretable AI for clinical transparency.