Lee Aric, Ong Wilson, Makmur Andrew, Ting Yong Han, Tan Wei Chuan, Lim Shi Wei Desmond, Low Xi Zhen, Tan Jonathan Jiong Hao, Kumar Naresh, Hallinan James T P D
Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore.
Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore.
Bioengineering (Basel). 2024 Sep 5;11(9):894. doi: 10.3390/bioengineering11090894.
Diagnostic imaging, particularly MRI, plays a key role in the evaluation of many spine pathologies. Recent progress in artificial intelligence and its subset, machine learning, has led to many applications within spine MRI, which we sought to examine in this review. A literature search of the major databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The search yielded 1226 results, of which 50 studies were selected for inclusion. Key data from these studies were extracted. Studies were categorized thematically into the following: Image Acquisition and Processing, Segmentation, Diagnosis and Treatment Planning, and Patient Selection and Prognostication. Gaps in the literature and the proposed areas of future research are discussed. Current research demonstrates the ability of artificial intelligence to improve various aspects of this field, from image acquisition to analysis and clinical care. We also acknowledge the limitations of current technology. Future work will require collaborative efforts in order to fully exploit new technologies while addressing the practical challenges of generalizability and implementation. In particular, the use of foundation models and large-language models in spine MRI is a promising area, warranting further research. Studies assessing model performance in real-world clinical settings will also help uncover unintended consequences and maximize the benefits for patient care.
诊断成像，尤其是磁共振成像（MRI），在许多脊柱疾病的评估中起着关键作用。人工智能及其子集机器学习的最新进展已在脊柱MRI领域产生了诸多应用，我们试图在本综述中对此进行研究。根据系统评价和Meta分析的首选报告项目（PRISMA）指南，对主要数据库（PubMed、MEDLINE、Web of Science、ClinicalTrials.gov）进行了文献检索。检索结果为1226项，其中50项研究被选中纳入。提取了这些研究的关键数据。研究按主题分为以下几类：图像采集与处理、分割、诊断与治疗规划以及患者选择与预后评估。讨论了文献中的空白以及未来研究的建议领域。当前研究表明，人工智能有能力改善该领域的各个方面，从图像采集到分析以及临床护理。我们也认识到当前技术的局限性。未来的工作需要共同努力，以便在应对可推广性和实施等实际挑战的同时充分利用新技术。特别是，基础模型和大语言模型在脊柱MRI中的应用是一个有前景的领域，值得进一步研究。评估模型在真实临床环境中性能的研究也将有助于发现意外后果，并最大限度地提高对患者护理的益处。
