机器学习在慢性疼痛脊髓刺激中的应用。
Machine Learning in Spinal Cord Stimulation for Chronic Pain.
机构信息
Department of Clinical Neurosciences, Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, Florida, USA.
Department of Neurosurgery, Albany Medical College, Albany, New York, USA.
出版信息
Oper Neurosurg (Hagerstown). 2023 Aug 1;25(2):112-116. doi: 10.1227/ons.0000000000000774. Epub 2023 May 22.
Spinal cord stimulation (SCS) is an effective treatment for chronic neuropathic pain. The success of SCS is dependent on candidate selection, response to trialing, and programming optimization. Owing to the subjective nature of these variables, machine learning (ML) offers a powerful tool to augment these processes. Here we explore what work has been done using data analytics and applications of ML in SCS. In addition, we discuss aspects of SCS which have narrowly been influenced by ML and propose the need for further exploration. ML has demonstrated a potential to complement SCS to an extent ranging from assistance with candidate selection to replacing invasive and costly aspects of the surgery. The clinical application of ML in SCS shows promise for improving patient outcomes, reducing costs of treatment, limiting invasiveness, and resulting in a better quality of life for the patient.
脊髓刺激 (SCS) 是治疗慢性神经性疼痛的有效方法。SCS 的成功取决于候选者的选择、对试验的反应和编程优化。由于这些变量具有主观性，机器学习 (ML) 提供了一个强大的工具来增强这些过程。在这里，我们探讨了使用数据分析和 ML 在 SCS 中的应用所做的工作。此外，我们还讨论了 ML 对 SCS 的影响较小的方面，并提出了进一步探索的需要。ML 已经证明具有在一定程度上补充 SCS 的潜力，从协助候选者的选择到替代手术的侵入性和昂贵方面。ML 在 SCS 中的临床应用有望改善患者的治疗效果，降低治疗成本，减少侵入性，并提高患者的生活质量。
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