Zhao Peng, Zhu Shan
Tianjin Hospital, Tianjin, China.
Eur Spine J. 2025 Jul 25. doi: 10.1007/s00586-025-09179-z.
Intervertebral disc degeneration (IDD) is a major contributor to chronic low back pain. Magnetic resonance imaging (MRI) serves as the gold standard for IDD assessment, yet manual grading is often subjective and inconsistent. With advances in artificial intelligence (AI), particularly deep learning, automated detection and classification of IDD from MRI has become increasingly feasible. This narrative review aims to provide a comprehensive overview of AI applications-especially machine learning and deep learning techniques-for MRI-based detection and grading of lumbar disc degeneration, highlighting their clinical value, current limitations, and future directions.
Relevant studies were reviewed and summarized based on thematic structure. The review covers classical methods (e.g., support vector machines), deep learning models (e.g., CNNs, SpineNet, ResNet, U-Net), and hybrid approaches incorporating transformers and multitask learning. Technical details, model architectures, performance metrics, and representative datasets were synthesized and discussed.
AI systems have demonstrated promising performance in automatic IDD grading, in some cases matching or surpassing expert radiologists. CNN-based models showed high accuracy and reproducibility, while hybrid models further enhanced segmentation and classification tasks. However, challenges remain in generalizability, data imbalance, interpretability, and regulatory integration. Tools such as Grad-CAM and SHAP improve model transparency, while methods like few-shot learning and data augmentation can alleviate data limitations.
AI-assisted analysis of MRI for lumbar disc degeneration offers significant potential to enhance diagnostic efficiency and consistency. While current models are encouraging, real-world clinical implementation requires further advancements in interpretability, data diversity, ethical standards, and large-scale validation.
椎间盘退变（IDD）是慢性下腰痛的主要原因。磁共振成像（MRI）是IDD评估的金标准，但人工分级往往主观且不一致。随着人工智能（AI）的发展，尤其是深度学习，从MRI自动检测和分类IDD变得越来越可行。本叙述性综述旨在全面概述基于MRI的腰椎间盘退变检测和分级的AI应用——尤其是机器学习和深度学习技术，强调其临床价值、当前局限性和未来方向。
根据主题结构对相关研究进行回顾和总结。综述涵盖经典方法（如支持向量机）、深度学习模型（如卷积神经网络、SpineNet、ResNet、U-Net）以及结合变压器和多任务学习的混合方法。综合并讨论了技术细节、模型架构、性能指标和代表性数据集。
AI系统在自动IDD分级方面表现出了有前景的性能，在某些情况下与专家放射科医生相当或更优。基于卷积神经网络的模型显示出高准确性和可重复性，而混合模型进一步增强了分割和分类任务。然而，在通用性、数据不平衡、可解释性和监管整合方面仍存在挑战。诸如Grad-CAM和SHAP等工具提高了模型透明度，而少样本学习和数据增强等方法可以缓解数据限制。
AI辅助分析MRI用于腰椎间盘退变具有显著潜力，可提高诊断效率和一致性。虽然当前模型令人鼓舞，但实际临床应用需要在可解释性、数据多样性、伦理标准和大规模验证方面取得进一步进展。
