Department of Neurological Surgery, University of California San Diego, La Jolla, CA, USA.
Department of Neurosurgery, New York University, New York, NY, USA.
Spine J. 2021 Oct;21(10):1626-1634. doi: 10.1016/j.spinee.2021.04.019. Epub 2021 May 8.
As we experience a technological revolution unlike any other time in history, spinal surgery as a discipline is poised to undergo a dramatic transformation. As enormous amounts of data become digitized and more readily available, medical professionals approach a critical juncture with respect to how advanced computational techniques may be incorporated into clinical practices. Within neurosurgery, spinal disorders in particular, represent a complex and heterogeneous disease entity that can vary dramatically in its clinical presentation and how it may impact patients' lives. The spectrum of pathologies is extremely diverse, including many different etiologies such as trauma, oncology, spinal deformity, infection, inflammatory conditions, and degenerative disease among others. The decision to perform spine surgery, especially complex spine surgery, involves several nuances due to the interplay of biomechanical forces, bony composition, neurologic deficits, and the patient's desired goals. Adult spinal deformity as an example is one of the most complex, given its involvement of not only the spine, but rather the entirety of the skeleton in order to appreciate radiographic completeness. With the vast array of variables contributing to spinal disorders, treatment algorithms can vary significantly, and it is very difficult for surgeons to predict how patients will respond to surgery. As such, it will become imperative for spine surgeons to utilize the burgeoning availability of advanced computational tools to process unprecedented amounts of data and provide novel insights into spinal disease. These tools range from predictive models built using machine learning algorithms, to deep learning methods for imaging analysis, to natural language processing that can mine text from electronic medical records or transcribed patient visits - all to better treat the intricacies of spinal disorders. The adoption of such techniques will empower patients and propel spine surgeons into the era of personalized medicine, by allowing clinical plans to be tailored to address individual patients' needs. This paper, which exists in the context of a larger body of literatutre, provides a comprehensive review of the current state and future of artificial intelligence and machine learning with a particular emphasis on Adult spinal deformity surgery.
随着我们经历着一场前所未有的技术革命，脊柱外科作为一门学科正准备发生巨大的转变。随着大量数据的数字化和更易于获取，医疗专业人员在如何将先进的计算技术纳入临床实践方面正面临着一个关键的转折点。在神经外科中，脊柱疾病，尤其是脊柱疾病，代表了一种复杂而异质的疾病实体，其临床表现和对患者生活的影响可能有很大的不同。病变的范围极其多样化，包括许多不同的病因，如创伤、肿瘤、脊柱畸形、感染、炎症性疾病和退行性疾病等。决定进行脊柱手术，特别是复杂的脊柱手术，由于生物力学力、骨骼成分、神经缺陷以及患者的预期目标的相互作用，涉及到几个细微差别。例如，成人脊柱畸形就是一个非常复杂的例子，因为它不仅涉及脊柱，还涉及整个骨骼，以便全面评估放射学的完整性。由于导致脊柱疾病的变量众多，治疗方案可能会有很大的差异，外科医生很难预测患者对手术的反应。因此，脊柱外科医生将必须利用新兴的高级计算工具的可用性来处理前所未有的大量数据，并为脊柱疾病提供新的见解。这些工具的范围从使用机器学习算法构建的预测模型，到用于图像分析的深度学习方法，再到可以从电子病历或转录的患者就诊记录中挖掘文本的自然语言处理，所有这些都是为了更好地治疗脊柱疾病的复杂性。采用这些技术将使患者受益，并使脊柱外科医生进入个性化医疗时代，使临床计划能够根据个体患者的需求进行定制。本文是在更广泛的文献背景下提出的，全面回顾了人工智能和机器学习的现状和未来，特别强调了成人脊柱畸形手术。
