Liawrungrueang Wongthawat, Park Jong-Beom, Cholamjiak Watcharaporn, Sarasombath Peem, Riew K Daniel
Department of Orthopaedics, School of Medicine, University of Phayao, Phayao, Thailand.
Department of Orthopaedic Surgery, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, Korea.
Global Spine J. 2025 Mar;15(2):1405-1418. doi: 10.1177/21925682241274372. Epub 2024 Aug 15.
Systematic review.
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.
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.
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.
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.
系统评价。
腰椎退行性椎间盘疾病（DDD）给全球医疗保健带来了重大挑战，使用传统方法难以进行准确诊断。人工智能（AI），尤其是机器学习和深度学习，为提高腰椎DDD的诊断准确性和工作流程提供了有前景的工具。本研究旨在综述AI辅助磁共振成像（MRI）在腰椎DDD诊断中的应用，并讨论当前临床应用的研究情况。
按照系统评价和Meta分析的首选报告项目（PRISMA）指南，对电子数据库进行系统检索，以确定AI在基于MRI的腰椎DDD诊断中的应用研究。检索词包括“人工智能”“机器学习”“深度学习”“腰痛”“腰椎”“椎间盘”“退变”和“MRI”的组合，检索2010年1月1日至2024年1月1日期间的英文研究。纳入标准包括同行评审期刊中的实验性和观察性研究。数据提取重点关注研究特征、AI技术、性能指标和诊断结果，并使用预定义标准评估质量。
20项研究符合纳入标准，采用了包括机器学习和深度学习在内的各种AI方法，以诊断腰椎DDD的表现，如椎间盘退变、突出和膨出。AI模型在准确性、敏感性和特异性方面始终优于传统方法，不同诊断目标的性能指标范围为71.5%至99%。
该算法模型为将AI整合到常规临床实践中提供了一个结构化框架，提高了腰椎DDD管理中的诊断精度和患者预后。需要进一步的研究和验证，以完善AI算法在腰椎DDD诊断中的实际应用。
