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

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. Cervical spondylosis is a highly prevalent degenerative disease that lacks visible lesions on medical images, making its diagnosis heavily reliant on scarce expert experience. Here the authors show a cascade-ensemble deep learning framework that achieves expert-level accuracy in diagnosing cervical degeneration by integrating multi-modal imaging data and analyzing key clinical indicators