Trento Alessandro, Rapisarda Salvatore, Bresolin Nicola, Valenti Andrea, Giordan Enrico
Department of Neuroscience, University of Verona, 37126 Verona, Italy.
Department of Neuroscience, University of Padua, 35128 Padua, Italy.
Medicina (Kaunas). 2025 Aug 1;61(8):1400. doi: 10.3390/medicina61081400.
In this narrative review, we explore the role of artificial intelligence (AI) in managing lumbar degenerative conditions, a topic that has recently garnered significant interest. The use of AI-based solutions in spine surgery is particularly appealing due to its potential applications in preoperative planning and outcome prediction. This study aims to clarify the impact of artificial intelligence models on the diagnosis and prognosis of common types of degenerative conditions: lumbar disc herniation, spinal stenosis, and eventually spinal fusion. Additionally, the study seeks to identify predictive factors for lumbar fusion surgery based on a review of the literature from the past 10 years. From the literature search, 96 articles were examined. The literature on this topic appears to be consistent, describing various models that show promising results, particularly in predicting outcomes. However, most studies adopt a retrospective approach and often lack detailed information about imaging features, intraoperative findings, and postoperative functional metrics. Additionally, the predictive performance of these models varies significantly, and few studies include external validation. The application of artificial intelligence in treating degenerative spine conditions, while valid and promising, is still in a developmental phase. However, over the last decade, there has been an exponential growth in studies related to this subject, which is beginning to pave the way for its systematic use in clinical practice.
在这篇叙述性综述中，我们探讨了人工智能（AI）在管理腰椎退行性疾病中的作用，这是一个最近引起广泛关注的话题。基于人工智能的解决方案在脊柱手术中的应用特别具有吸引力，因为其在术前规划和结果预测方面具有潜在应用价值。本研究旨在阐明人工智能模型对常见类型退行性疾病（腰椎间盘突出症、椎管狭窄症以及最终的脊柱融合术）的诊断和预后的影响。此外，该研究还试图通过回顾过去10年的文献来确定腰椎融合手术的预测因素。通过文献检索，共审查了96篇文章。关于这个主题的文献似乎是一致的，描述了各种显示出有前景结果的模型，特别是在预测结果方面。然而，大多数研究采用回顾性方法，并且往往缺乏关于影像特征、术中发现和术后功能指标的详细信息。此外，这些模型的预测性能差异很大，很少有研究包括外部验证。人工智能在治疗退行性脊柱疾病中的应用虽然有效且有前景，但仍处于发展阶段。然而，在过去十年中，与该主题相关的研究呈指数级增长，这开始为其在临床实践中的系统应用铺平道路。
