ArticleFrontiers in neuroscience2024
Deep learning models for MRI-based clinical decision support in cervical spine degenerative diseases.
Article in Frontiers in neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning and Deep Learning for the Diagnosis of Cervical Degenerative Diseases: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Development and multi-institutional validation of a deep learning algorithm for predicting cervical cord compression using dynamic cervical lateral radiographs.Scientific reports · 2026Article
- Feasibility of fully automatic assessment of cervical canal stenosis using MRI via deep learning.Quantitative imaging in medicine and surgery · 2025Article
- A multi-graph convolutional network method for Alzheimer's disease diagnosis based on multi-frequency EEG data with dual-mode connectivity.Frontiers in neuroscience · 2025Article
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Authors and funding
11 authors.
Funding
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Abstract
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 hospitalized 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.
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