Application and performance of deep learning models for the automated diagnosis of cervical central spinal stenosis on MRI: a systematic review

Deep-learning shows promising diagnostic performance for automated CCSS assessment on MRI and could reduce variability and reporting time. However, generalisability remains uncertain due to small, retrospective, largely single-centre cohorts and scarce external validation. Standardized reporting (e.g., CLAIM) and prospective, multi-centre validation is needed before routine clinical deployment.

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Publication Details

Journal
Brain and Spine
Published
2025-12-19
DOI
https://doi.org/10.1016/j.bas.2025.105902
Primary Topic
Cervical and Thoracic Myelopathy
Type
article
Field-Weighted Citation Impact
0.00
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article

Application and performance of deep learning models for the automated diagnosis of cervical central spinal stenosis on MRI: a systematic review

Brain and Spine
Cervical and Thoracic Myelopathy
article

Application and performance of deep learning models for the automated diagnosis of cervical central spinal stenosis on MRI: a systematic review

article en

Abstract

Deep-learning shows promising diagnostic performance for automated CCSS assessment on MRI and could reduce variability and reporting time. However, generalisability remains uncertain due to small, retrospective, largely single-centre cohorts and scarce external validation. Standardized reporting (e.g., CLAIM) and prospective, multi-centre validation is needed before routine clinical deployment.

Brain and SpineVol. 6
German Cancer Research Center (DE), Heidelberg University (DE), University Hospital Heidelberg (DE)
Openalex Percentile: Top 28%
Cervical and Thoracic Myelopathy
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