Lateral mass screw fixation in cervical spine injury

Rehman, Lal; Bukhari, Iram; Afzal, Ali R.; Rizvi, Raza

doi:10.12669/pjms.336.12947

Cited by 3 publications

(1 citation statement)

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“…Some researchers have proposed that, combining pixel information of different scales can extract the best size information; some researchers believe that, reducing the size of the convolution kernel can improve the running speed of the neural network. [7][8][9][10] The CT image reconstruction algorithm can effectively remove image noise and improve image quality, assisting physicians in diagnosing diseases. With the rapid development of deep learning, many deep learning networks are used to process CT images.…”

Section: Resultsmentioning

confidence: 99%

Deep Learning Algorithm Based on Analysis the Effect of Posterior Cervical Vertebral Canal Decompression Angioplasty in the Treatment of Ossification of Posterior Longitudinal Ligament of Cervical Spine by CT Image

Liu¹,

Kong²

2021

Pak J Med Sci

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Original Article Deep learning algorithm based on analyzing the effect of posterior cervical vertebral canal decompression angioplastyin the treatment of ossification of posterior longitudinalligament of cervical spine by CT image Yang Liu1, Jianjun Kong2 ABSTRACTObjective: 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. Methods: 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. Results: 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.Conclusion: 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. KEYWORDS: Cervical spine, Ossification of posterior longitudinal ligament, Hyperextension injury, Spinal stenosis rate, JOA improvement rate. doi: https://doi.org/10.12669/pjms.37.6-WIT.4857How to cite this:Liu Y, Kong J. Deep learning algorithm based on analyzing the effect of posterior cervical vertebral canal decompression angioplasty in the treatment of ossification of posterior longitudinal ligament of cervical spine by CT image. Pak J Med Sci. 2021;37(6):1630-1635. doi: https://doi.org/10.12669/pjms.37.6-WIT.4857 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Section: Resultsmentioning

confidence: 99%