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• Reviews •
Research progress on AI-based multimodal MRI in the diagnosis and treatment decision-making of cervical spondylotic myelopathy
DAI Dacheng  XIE Fangfang  CUI Jiahe  WANG Siyu  CAI Junhao  YAO Fei  WANG Guimao 

Cite this article as DAI D C, XIE F F, CUI J H, et al. Research progress on AI-based multimodal MRI in the diagnosis and treatment decision-making of cervical spondylotic myelopathy[J]. Chin J Magn Reson Imaging, 2026, 17(8): 226-234. DOI:10.12015/issn.1674-8034.2026.08.027.


[Abstract] Artificial intelligence (AI) technologies, spearheaded by deep learning (DL), have emerged as a prominent research focus in neuroimaging due to their remarkable capabilities in extracting complex image features and analyzing nonlinear data. In the diagnosis and management of cervical spondylotic myelopathy (CSM), AI is no longer confined to automated image segmentation, but rather encompasses key stages including quantitative assessment, multimodal feature analysis, and functional prognosis prediction. It facilitates not only early and precise lesion identification with objective grading, overcoming the lack of objectivity in conventional MRI manual visual evaluation, but also assists in forecasting postoperative neurological recovery trends, thereby supporting clinicians in devising individualized surgical plans and rehabilitation strategies. Although some studies have explored the application of AI in cervical spine diseases, existing reviews are mostly limited to morphological analysis of a single conventional magnetic resonance imaging (MRI) sequence or a single diagnostic stage, lacking a systematic summary regarding the application value of the deep integration of multimodal MRI and AI in CSM, such as diffusion tensor imaging, diffusion basis spectrum imaging, and resting-state functional MRI. In particular, insufficient attention has been paid to how AI reveals the micro-pathological evolution of the spinal cord and the functional remodeling of the "brain-spinal cord axis". To fill this gap, this review synthesizes recent advances in AI-integrated multimodal MRI for early screening, auxiliary diagnosis, and prognosis prediction in CSM, aiming to explore current challenges and future prospects within this domain from the perspectives of pathophysiological representation and full-chain diagnostic and treatment decision-making, with the hope of providing new insights and academic references for the clinical translation practice of AI-based multimodal MRI technology in the precision diagnosis and treatment of CSM.
[Keywords] cervical spondylotic myelopathy；artificial intelligence；magnetic resonance imaging；deep learning；diagnosis and treatment decision-making；prognosis prediction

DAI Dacheng1   XIE Fangfang2   CUI Jiahe1   WANG Siyu1   CAI Junhao1   YAO Fei1, 2   WANG Guimao1*  

1 Department of Tuina, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200071, China

2 School of Acupuncture-Moxibustion and Tuina, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China

Corresponding author: WANG G M, E-mail: wgm613@sina.com

Conflicts of interest   None.

Received  2026-02-06
Accepted  2026-07-24
DOI: 10.12015/issn.1674-8034.2026.08.027
Cite this article as DAI D C, XIE F F, CUI J H, et al. Research progress on AI-based multimodal MRI in the diagnosis and treatment decision-making of cervical spondylotic myelopathy[J]. Chin J Magn Reson Imaging, 2026, 17(8): 226-234. DOI:10.12015/issn.1674-8034.2026.08.027.

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