磁共振成像2026，Vol.17Issue(8)：226-234,9.DOI:10.12015/issn.1674-8034.2026.08.027
基于人工智能的多模态MRI在脊髓型颈椎病诊疗决策中的研究进展
Research progress on AI-based multimodal MRI in the diagnosis and treatment decision-making of cervical spondylotic myelopathy
摘要
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.关键词
脊髓型颈椎病/人工智能/磁共振成像/深度学习/诊疗决策/预后预测Key words
cervical spondylotic myelopathy/artificial intelligence/magnetic resonance imaging/deep learning/diagnosis and treatment decision-making/prognosis prediction分类
医药卫生引用本文复制引用
戴大城,谢芳芳,崔家禾,汪思宇,蔡君豪,姚斐,王桂茂..基于人工智能的多模态MRI在脊髓型颈椎病诊疗决策中的研究进展[J].磁共振成像,2026,17(8):226-234,9.基金项目
National Natural Science Foundation of China(No.82575252) （No.82575252）
Shanghai University of Traditional Chinese Medicine(No.02.ZY05.K231201N). 国家自然科学基金项目(编号:82575252) （No.02.ZY05.K231201N）
上海中医药大学项目(编号:02.ZY05.K231201N) （编号:02.ZY05.K231201N）
