Advances and challenges in AI-assisted MRI for lumbar disc degeneration detection and classification

PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.