A review of the application of novel intervertebral disc diagnostic technologies integrated with artificial intelligence in medical imaging.

Intervertebral disc diseases are a leading cause of chronic low back pain and disability worldwide. Conventional imaging diagnostic techniques-such as X-ray, CT, and MRI-exhibit limitations in diagnostic accuracy, efficiency, and other aspects. This review examines recent advances in artificial intelligence (AI)-integrated medical imaging for diagnosing intervertebral disc disorders. We first assess the current roles and limitations of conventional imaging modalities-X-ray, CT, and MRI-before delving into the technical foundations of machine learning (ML) and deep learning (DL) in this field. The review also surveys the current state of AI applications in spinal imaging, detailing specific implementations of AI combined with X-ray, CT, and MRI. Both common multi-modal approaches and distinctive single-modal applications are examined. Additionally, the review addresses current challenges in AI technology, including constrained sample size and quality, as well as limitations in model performance. It concludes by outlining promising future pathways, including multi-modal data fusion and the development of end-to-end diagnostic workflows, which support the translation of efficient, standardized AI tools into clinical practice.