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<p>Cervical spondylosis is one of the most common degenerative diseases, seriously affecting life quality. Unlike diseases with explicit lesions like cancer, hydroncus, or fracture, the degeneration of the cervical spine cannot be explicitly detected from the appearance of medical images, requiring extensive experience of doctors to interpret subtle clues. However, the extremely high incidence of cervical spondylosis coincides with a serious shortage of experienced doctors and uneven distribution of medical resources, hindering early diagnosis. We propose a cascade-ensemble deep learning framework for cervical spondylosis diagnosis. The framework integrates vertebral body detection and degenerative diagnosis through a cascading architecture, and jointly trains an ensemble of degenerative indicators in a multi-task learning manner. We demonstrate that deep learning models are more sensitive to distance and position based indicators than angle based ones. In intervertebral stenosis analysis, our method achieves comparable performance to senior radiologists and clinicians, with much faster diagnostic speed.</p>

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Automated diagnostic of cervical spondylosis on multimodal medical images with a multi-task deep learning model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月5日

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  • Xinhang Song,
  • Yuan Li,
  • Hanqiang Ouyang,
  • Meiyi Yao,
  • Fangbo Zhao,
  • Kecen Yu,
  • Shuai Tian,
  • Xiaoying Xing,
  • Yan Zhou,
  • Enlong Zhang,
  • Ming Ni,
  • Yuan Yuan,
  • Dasheng Li,
  • Ning Lang,
  • Liang Jiang,
  • Huishu Yuan,
  • Shuqiang Jiang

摘要

Cervical spondylosis is one of the most common degenerative diseases, seriously affecting life quality. Unlike diseases with explicit lesions like cancer, hydroncus, or fracture, the degeneration of the cervical spine cannot be explicitly detected from the appearance of medical images, requiring extensive experience of doctors to interpret subtle clues. However, the extremely high incidence of cervical spondylosis coincides with a serious shortage of experienced doctors and uneven distribution of medical resources, hindering early diagnosis. We propose a cascade-ensemble deep learning framework for cervical spondylosis diagnosis. The framework integrates vertebral body detection and degenerative diagnosis through a cascading architecture, and jointly trains an ensemble of degenerative indicators in a multi-task learning manner. We demonstrate that deep learning models are more sensitive to distance and position based indicators than angle based ones. In intervertebral stenosis analysis, our method achieves comparable performance to senior radiologists and clinicians, with much faster diagnostic speed.