Cervical Spondylosis (CS), a common degenerative disease in older populations, has historically presented diagnostic challenges. Subtle changes in vertebrae are difficult to detect from medical images, often requiring highly experienced doctors. Misdiagnosis can lead to poor patient outcomes.
Symptoms range from neck and arm pain to gait disturbances. The condition's varied causes further complicate diagnosis, leading to different imaging presentations, including spinal curve loss, instability, and nerve compression.
A retrospective study analyzed X-ray and MRI scans from CS patients, with a mean age of 54. A deep learning model, trained on multimodal imaging data, performed on par with senior radiologists but with significantly greater diagnostic efficiency.
This AI model could improve diagnostic accuracy and efficiency, crucial as European populations age and CS prevalence potentially rises, even in younger demographics due to modern lifestyles. However, the dataset is not yet publicly available and has a predominance of male subjects, raising concerns about potential bias and the need for more diverse data in AI development.