Shi Liangyu, Wang Hongfei, Shea Graham Ka-Hon
From the Department of Orthopaedics and Traumatology, Li Ka Shing University, The University of Hong Kong, Hong Kong SAR, China.
J Am Acad Orthop Surg Glob Res Rev. 2025 Apr 10;9(4). doi: 10.5435/JAAOSGlobal-D-24-00405. eCollection 2025 Apr 1.
A comprehensive review on the application of artificial intelligence (AI) within spine surgery as a specialty remains lacking.
This scoping review was conducted upon PubMed and EMBASE databases according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Our analysis focused on publications from January 1, 2020, to March 31, 2024, with a specific focus on AI in the field of spine surgery. Review articles and articles predominantly concerning secondary validation of algorithms, medical physics, electronic devices, biomechanics, preclinical, and with a lack of clinical emphasis were excluded.
One hundred five studies were included after our inclusion/exclusion criteria were applied. Most studies (n = 100) were conducted through supervised learning upon prelabeled data sets. Overall, 38 studies used conventional machine learning methods upon predefined features, whereas 67 used deep learning methods, predominantly for medical image analyses. Only 25.7% of studies (27/105) collected data from more than 1,000 patients for model development and validation. Data originated from only a single center in 72 studies. The most common application was prognostication (38/105), followed by diagnosis (35/105), medical image processing (29/105), and surgical assistance (3/105).
The application of AI within the domain of spine surgery has significant potential to advance patient-specific diagnosis, management, and surgical execution.
目前仍缺乏对人工智能（AI）在脊柱外科专业领域应用的全面综述。
根据系统评价与Meta分析的首选报告项目指南，在PubMed和EMBASE数据库上进行了这项范围综述。我们的分析重点关注2020年1月1日至2024年3月31日期间的出版物，特别关注脊柱外科领域的人工智能。排除综述文章以及主要涉及算法二次验证、医学物理、电子设备、生物力学、临床前研究且缺乏临床重点的文章。
应用纳入/排除标准后，共纳入105项研究。大多数研究（n = 100）是通过对预先标记的数据集进行监督学习开展的。总体而言，38项研究在预定义特征上使用传统机器学习方法，而67项使用深度学习方法，主要用于医学图像分析。只有25.7%的研究（27/105）收集了超过1000名患者的数据用于模型开发和验证。72项研究的数据仅来自单一中心。最常见的应用是预后预测（38/105），其次是诊断（35/105）、医学图像处理（29/105）和手术辅助（3/105）。
人工智能在脊柱外科领域的应用具有推进针对患者的诊断、管理和手术执行的巨大潜力。
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