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<p>This study introduces an innovative method for early ultrasound classification of developmental dysplasia of the hip (DDH) in infants, integrating key point detection based on the YOLOv8 model with radiomics. Leveraging YOLOv8’s advanced capabilities, the method accurately identifies and locates key anatomical landmarks in pediatric hip ultrasound images. Constructing angular information through these key points serves as a crucial clinical feature for DDH classification. By fusing these clinical features with radiomic features, the method further characterizes hip joint morphology and function. The aim is to enhance the accuracy and reliability of early DDH classification, facilitating timely intervention and improving patient prognosis. Preliminary results demonstrate the feasibility and potential of this integrated approach, with an average α Angle Difference of 3.13 degrees and an average β Angle Difference of 4.83 degrees in hip DDH imaging. The model’s automatic calculation of α and β angles is comparable to clinicians, alleviating clinical burden while providing valuable clinical features for DDH classification. Furthermore, based on deep learning analysis combining clinical and radiomic features, the model has achieved impressive results on multi-center test sets, with an accuracy, recall, precision, and F1 score of 0.8544, 0.8321, 0.8465, and 0.8383 respectively. </p>

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A method for early ultrasound classification of developmental dysplasia of the hip in infants: combining YOLOv8-based keypoint detection with radiomics

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月8日

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  • Zhenhong Zhu,
  • Yi Zheng,
  • Ting Feng,
  • Yanhong Lu,
  • Jisu Hu,
  • Xusheng Qian,
  • Zhiyong Zhou,
  • Yakang Dai,
  • Xiaodong Wang

摘要

This study introduces an innovative method for early ultrasound classification of developmental dysplasia of the hip (DDH) in infants, integrating key point detection based on the YOLOv8 model with radiomics. Leveraging YOLOv8’s advanced capabilities, the method accurately identifies and locates key anatomical landmarks in pediatric hip ultrasound images. Constructing angular information through these key points serves as a crucial clinical feature for DDH classification. By fusing these clinical features with radiomic features, the method further characterizes hip joint morphology and function. The aim is to enhance the accuracy and reliability of early DDH classification, facilitating timely intervention and improving patient prognosis. Preliminary results demonstrate the feasibility and potential of this integrated approach, with an average α Angle Difference of 3.13 degrees and an average β Angle Difference of 4.83 degrees in hip DDH imaging. The model’s automatic calculation of α and β angles is comparable to clinicians, alleviating clinical burden while providing valuable clinical features for DDH classification. Furthermore, based on deep learning analysis combining clinical and radiomic features, the model has achieved impressive results on multi-center test sets, with an accuracy, recall, precision, and F1 score of 0.8544, 0.8321, 0.8465, and 0.8383 respectively.