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Osteoporosis is a bone disorder characterized by weakened bone density, increasing fracture risk, especially in the knee. Early detection is challenging due to the lack of standardized image-based tools and automated analysis. The absence of radiomic feature datasets impairs result reproducibility. To address this, we introduce RADXOst, a radiomic feature-based X-ray dataset for knee osteoporosis detection. The dataset is evaluated its effectiveness using multiple machine learning models with K-fold cross-validation, analyze the results, and recommend suitable methods for osteoporosis detection.

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Osteoporosis Detection Using Radiomic Features: An Effective Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Akangkhi Borah,
  • Lakhya Borah,
  • Dhruba Kumar Bhattacharyya,
  • Diganta Apurba Phukan,
  • Sanjeev Kumar Handique

摘要

Osteoporosis is a bone disorder characterized by weakened bone density, increasing fracture risk, especially in the knee. Early detection is challenging due to the lack of standardized image-based tools and automated analysis. The absence of radiomic feature datasets impairs result reproducibility. To address this, we introduce RADXOst, a radiomic feature-based X-ray dataset for knee osteoporosis detection. The dataset is evaluated its effectiveness using multiple machine learning models with K-fold cross-validation, analyze the results, and recommend suitable methods for osteoporosis detection.