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Multi-modal and Multi-view Cervical Spondylosis Imaging Dataset.

Qi-Shuai Yu1,2, Jing-Yang Shan3,4, Jie Ma1

  • 1Department of Neurosurgery, the First Medical Center, Chinese PLA General Hospital, Beijing, China.

Scientific Data
|July 2, 2025
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Summary
This summary is machine-generated.

This study introduces the MMCSD, a new multi-modal imaging dataset for cervical spondylosis. It was used to develop a deep learning model predicting postoperative neck pain, aiding clinical decisions.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Orthopedics

Background:

  • Cervical spondylosis diagnosis and assessment rely heavily on multi-modal and multi-view imaging.
  • Deep learning (DL) shows promise for improving clinical management and research in cervical spondylosis.
  • A publicly available dataset is crucial for developing and validating DL models.

Purpose of the Study:

  • To introduce the Multi-Modal Cervical Spondylosis Dataset (MMCSD), a novel resource for cervical spondylosis research.
  • To develop and validate a DL model for predicting postoperative neck pain using the MMCSD.
  • To demonstrate the utility of the MMCSD in advancing DL applications for cervical spondylosis and neck pain.

Main Methods:

  • Compilation of a multi-modal and multi-view imaging dataset (MMCSD) from 250 cervical spondylosis patients.
  • Inclusion of various imaging modalities: MRI (sagittal T1/T2, axial T2) and CT (axial bone/soft tissue windows).
  • Development of a DL model utilizing the MMCSD to predict postoperative neck pain.

Main Results:

  • The MMCSD dataset was successfully created and shared publicly.
  • A DL model was developed using the MMCSD, demonstrating its capability in predicting postoperative neck pain.
  • The study validated the usability of the MMCSD for DL model development and clinical application.

Conclusions:

  • The MMCSD is a valuable resource for advancing DL research in cervical spondylosis.
  • The developed DL model shows potential for improving the prediction of postoperative neck pain.
  • This work facilitates better clinical diagnostic assessments and treatment decision-making for cervical spondylosis and associated neck pain.