A Comprehensive Review of Deep Learning Techniques for Spine and Intervertebral Disc Imaging

Deep learning (DL) has emerged as a powerful tool for automated analysis of spine and intervertebral disc (IVD) imaging, offering promising solutions for detection, segmentation and classification tasks in magnetic resonance imaging and computed tomography modalities. Accurate interpretation of spinal images is clinically important but remains a challenging task due to anatomical complexity, image variability and inter-observer differences in manual assessment. In order to understand the applicability of DL models in spine and IVD imaging, this review provides a structured and critical overview of recent DL-based research articles. Unlike existing surveys, this work emphasizes a comparative analysis of model architectures, dataset sources and characteristics, evaluation metrics, validation strategies, and clinical applicability. Key challenges related to dataset heterogeneity, limited external validation, inconsistent performance reporting, and barriers to clinical adoption are systematically discussed. The review also highlights emerging research directions, including standardized benchmarks, multi-contrast and multimodal learning, explainable artificial intelligence, and robust cross-institutional validation to support the development of reliable and clinically deployable spine imaging systems.