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ORIGINAL RESEARCH article

Front. Neurosci.
Sec. Neurodegeneration
Volume 18 - 2024 | doi: 10.3389/fnins.2024.1501972

Deep Learning Models for MRI-Based Clinical Decision Support in Cervical Spine Degenerative Diseases

Provisionally accepted
Kai-Yu Li Kai-Yu Li 1Zhe-Yang Lu Zhe-Yang Lu 2Yu-Han Tian Yu-Han Tian 2Xiao Peng Liu Xiao Peng Liu 1Ye-Kai Zhang Ye-Kai Zhang 1Jia-Wei Qiu Jia-Wei Qiu 1Hua-Lin Li Hua-Lin Li 1Yu-Long Zhang Yu-Long Zhang 1Jia-Wei Huang Jia-Wei Huang 1Haobo Ye Haobo Ye 1Nai Feng Tian Nai Feng Tian 1*
  • 1 Department of Orthopaedic Surgery, Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
  • 2 Renji College of Wenzhou Medical University, Wenzhou, Zhejiang Province, China

The final, formatted version of the article will be published soon.

    Purpose: The purpose of our study is to develop a deep learning (DL) model based on MRI and analyze its consistency with the treatment recommendations for degenerative cervical spine disorders provided by the spine surgeons at our hospital. Methods: In this study, MRI of patients who were hospitalised for cervical spine degenerative disorders at our hospital from July 2023 to July 2024 were primarily collected. The dataset was divided into a training set, a validation set, and an external validation set. Four versions of the DL model were constructed. The external validation set was used to assess the consistency between the DL model and spine surgeons' recommendations about indication of cervical spine surgery regarding the dataset. Results: This study collected a total of 756 MR images from 189 patients. The external validation set included 30 patients and a total of 120 MR images, consisting of 43 images for grade 0, 20 images for grade 1, and 57 images for grade 2. The region of interest (ROI) detection model completed the ROI detection task perfectly. For the binary classification (grades 0 and 1, 2), DL version 1 showed the best consistency with the spine surgeons, achieving a Cohen's Kappa value of 0.874. DL version 4 also achieved nearly perfect consistency, with a Cohen's Kappa value of 0.811. For the three-class classification, DL version 1 demonstrated the best consistency with the spine surgeons, achieving a Cohen’s Kappa value of 0.743, while DL version 2 and DL version 4 also showed substantial consistency, with Cohen's Kappa values of 0.615 and 0.664, respectively. Conclusion: We initially developed deep learning algorithms that can provide clinical recommendations based on cervical spine MRI. The algorithm shows substantial consistency with experienced spine surgeons.

    Keywords: deep learning, Convolutional Neural Network, Magnetic Resonance Imaging, Cervical Spine Degenerative Diseases, Clinical decision

    Received: 26 Sep 2024; Accepted: 22 Nov 2024.

    Copyright: © 2024 Li, Lu, Tian, Liu, Zhang, Qiu, Li, Zhang, Huang, Ye and Tian. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Nai Feng Tian, Department of Orthopaedic Surgery, Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.