Author(s)

ZhongLiang Wang¹, Xiang Guo^2*

Affiliation(s)

¹School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
²Department of Spine Surgery, Shanghai Changzheng Hospital, Shanghai 200093, China.

Corresponding Author

Xiang Guo

ABSTRACT

Cervical ossification of the posterior longitudinal ligament (OPLL) is relatively common among Asian populations. Its progression can cause spinal canal stenosis and compression of the spinal cord and nerve roots, leading to neurological dysfunction and increasing surgical complexity and the risk of complications. In recent years, early identification, precise evaluation, and appropriate intervention for OPLL have become major focuses in radiology and spine surgery. Artificial intelligence, particularly deep learning, has shown new potential in the detection, lesion segmentation, and prognostic evaluation of this disease. This article integrates existing studies to summarize the advances of deep learning in multimodal imaging and discusses its value in clinical decision support, aiming to provide methodological references and clinical insights for related disciplines.

KEYWORDS

Deep learning; Cervical ossification of the posterior longitudinal ligament; Multimodal medical imaging; Risk prediction

CITE THIS PAPER

ZhongLiang Wang, Xiang Guo. Advances in the application of deep learning in cervical ossification of the posterior longitudinal ligament. Journal of Pharmaceutical and Medical Research. 2025, 7(3): 9-14. DOI: https://doi.org/10.61784/jpmr3049.

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