Liu Yang, Kong Jianjun
Yang Liu, Attending Physician. Department 9 of Orthopaedics, General Hospital of Jizhong Energy Xingtai Mining Group Co. Ltd., Xingtai, 054000, Hebei, China.
Jianjun Kong, Attending Physician. Department 9 of Orthopaedics, General Hospital of Jizhong Energy Xingtai Mining Group Co. Ltd., Xingtai, 054000, Hebei, China.
Pak J Med Sci. 2021;37(6):1630-1635. doi: 10.12669/pjms.37.6-WIT.4857.
The paper uses the convolutional neural network algorithm in the deep learning algorithm to explore the therapeutic effect of surgical treatment of hyperextension injuries associated with ossification of the posterior longitudinal ligament of the cervical spine.
In this retrospectively analyzed study 27 patients with hyperextension injury of the posterior longitudinal ligament of the cervical spine were selected from our hospital between August 2018 to July 2020. It included 21 males and 6 females; aged 36-79 years, with an average of 55.9 years.
Follow-up time of patients was 3-39 months, with an average of 17.4 months. The JOA score after surgery was significantly better than that before surgery (P<0.01), which was statistically significant; the improvement of JOA in patients undergoing anterior therapy was better than that in patients undergoing posterior therapy, which was statistically significant; the JOA improved in patients with minor violent injuries. The situation is significantly better than severe violent injuries, with statistical significance. The rate of postoperative JOA improvement was significantly correlated with the degree of nerve function retention of the injured spinal cord before surgery (P<0.01), and there was no significant correlation between the degree of spinal stenosis caused by ossification and the postoperative JOA improvement of patients.
Convolutional neural network algorithm in the deep learning algorithm based on the cervical spine posterior longitudinal ligament ossification hyperextension injury was significantly improved after surgery. The less preoperative neurological damage, the postoperative neurological function, the degree of improvement, there was no significant correlation between the degree of spinal stenosis and the improvement of postoperative spinal cord function. For patients with ossification of the posterior longitudinal ligament, if there are neurological symptoms, early surgical treatment is recommended to relieve the compression, so as to prevent irreversible neurological damage caused by trauma.
本文采用深度学习算法中的卷积神经网络算法,探讨颈椎后纵韧带骨化合并过伸伤手术治疗的疗效。
本研究为回顾性分析,选取2018年8月至2020年7月我院收治的27例颈椎后纵韧带过伸伤患者。其中男性21例,女性6例;年龄36 - 79岁,平均55.9岁。
患者随访时间为3 - 39个月,平均17.4个月。术后JOA评分明显优于术前(P<0.01),差异有统计学意义;前路治疗患者的JOA改善情况优于后路治疗患者,差异有统计学意义;轻度暴力损伤患者的JOA改善情况明显优于重度暴力损伤患者,差异有统计学意义。术后JOA改善率与术前脊髓损伤神经功能保留程度显著相关(P<0.01),而骨化所致椎管狭窄程度与患者术后JOA改善情况无明显相关性。
基于深度学习算法中的卷积神经网络算法,颈椎后纵韧带骨化合并过伸伤术后有明显改善。术前神经损伤越小,术后神经功能改善程度越好,椎管狭窄程度与术后脊髓功能改善情况无明显相关性。对于后纵韧带骨化患者,若出现神经症状,建议早期手术减压,以防止创伤导致不可逆的神经损伤。