Artificial Intelligence-Assisted MRI Diagnosis in Lumbar Degenerative Disc Disease: A Systematic Review

• Published in: Global spine journal Vol. 15; no. 2; pp. 1405 - 1418
• Main Authors: Liawrungrueang, Wongthawat, Park, Jong-Beom, Cholamjiak, Watcharaporn, Sarasombath, Peem, Riew, K. Daniel
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.03.2025; Sage Publications Ltd
• Subjects: Artificial intelligence; Business metrics; Deep learning; Degenerative disc disease; Global health; Machine learning; Magnetic resonance imaging; Systematic Review; deep learning; algorithm model; MRI diagnosis; machine learning; systematic review; artificial intelligence; lumbar degenerative disc disease
• ISSN: 2192-5682; 2192-5690; 2192-5690
• DOI: 10.1177/21925682241274372

Study Design Systematic review. Objectives Lumbar degenerative disc disease (DDD) poses a significant global health care challenge, with accurate diagnosis being difficult using conventional methods. Artificial intelligence (AI), particularly machine learning and deep learning, offers promising tools for improving diagnostic accuracy and workflow in lumbar DDD. This study aims to review AI-assisted magnetic resonance imaging (MRI) diagnosis in lumbar DDD and discuss current research for clinical use. Methods A systematic search of electronic databases identified studies on AI applications in MRI-based lumbar DDD diagnosis, following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Search terms included combinations of “Artificial Intelligence,” “Machine Learning,” “Deep Learning,” “Low Back Pain,” “Lumbar,” “Disc,” “Degeneration,” and “MRI,” targeting studies in English from January 1, 2010, to January 1, 2024. Inclusion criteria encompassed experimental and observational studies in peer-reviewed journals. Data extraction focused on study characteristics, AI techniques, performance metrics, and diagnostic outcomes, with quality assessed using predefined criteria. Results Twenty studies met the inclusion criteria, employing various AI methodologies, including machine learning and deep learning, to diagnose lumbar DDD manifestations such as disc degeneration, herniation, and bulging. AI models consistently outperformed conventional methods in accuracy, sensitivity, and specificity, with performance metrics ranging from 71.5% to 99% across different diagnostic objectives. Conclusion The algorithm model provides a structured framework for integrating AI into routine clinical practice, enhancing diagnostic precision and patient outcomes in lumbar DDD management. Further research and validation are needed to refine AI algorithms for real-world application in lumbar DDD diagnosis.