Lee Nathan J, Lombardi Joseph M, Lehman Ronald A
Department of Orthopaedics, Columbia University Medical Center, The Och Spine Hospital at New York-Presbyterian, New York, NY, USA
Department of Orthopaedics, Columbia University Medical Center, The Och Spine Hospital at New York-Presbyterian, New York, NY, USA.
Int J Spine Surg. 2023 Jun;17(S1):S18-S25. doi: 10.14444/8503. Epub 2023 May 16.
The complexity of patients with spine pathology and high rates of complications has driven extensive research directed toward optimizing outcomes and reducing complications. Traditional statistical analysis has been limited both in validity and in the number of predictor variables considered. Over the past decade, artificial intelligence and machine learning have taken center stage as the possible solution to creating more accurate and applicable patient-centered predictive models in spine surgery. This review discusses the current published machine learning applications on preoperative optimization, risk stratification, and predictive modeling for the cervical, lumbar, and adult spinal deformity populations.
脊柱疾病患者的复杂性和高并发症发生率推动了广泛的研究,旨在优化治疗结果并减少并发症。传统的统计分析在有效性和所考虑的预测变量数量方面都受到限制。在过去十年中,人工智能和机器学习已成为脊柱手术中创建更准确、更适用的以患者为中心的预测模型的可能解决方案。本文综述了当前已发表的关于颈椎、腰椎和成人脊柱畸形人群术前优化、风险分层和预测建模的机器学习应用。