Degenerative Cervical Myelopathy and Spinal Cord Injury: Introduction to the Special Issue

Soufi, Khadija; Nouri, Aria; Martín, Allan

doi:10.3390/jcm11154253

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…The significant reduction of the space available for the spinal cord results in decreased neurological function. [19][20][21][22][23][24][25][26][27] Common symptoms include tingling or numbness in the arms, fingers, or hands, as well as weakness in the arms, shoulders, or hands. Some patients also report trouble grasping and holding on to items.…”

Section: Discussionmentioning

confidence: 99%

Endoscopic Posterior Cervical Decompression for Ossified Posterior Longitudinal Ligament: A Technical Note

Zhang

Yan

et al. 2023

Int J Spine Surg

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Background: Ossification of the posterior longitudinal ligament (OPLL) may cause cervical myelopathy. In its multilevel form, it may not be easy to manage. Minimally invasive endoscopic posterior cervical decompression may be an alternative to traditional laminectomy surgery.Methods: Thirteen patients with multilevel OPLL and symptomatic cervical myelopathy were treated with endoscopic spine surgery from January 2019 to June 2020. In this consecutive observational cohort study, pre-and postoperative Japanese Orthopaedic Association (JOA) score and Neck Disability Index (NDI) were analyzed at a final follow-up of 2 years postoperatively.Results: There were 13 patients consisting of 3 women and 10 men. The patient's average age was 51.15 years. At the final 2-year follow-up, the JOA score improved from a preoperative value of 10.85 ± 2.91 to 14.77 ± 2.13 postoperatively (P < 0.001). The corresponding NDI scores decreased from 26.61 ± 12.88 to 11.12 ± 10.85 (P < 0.001). There were no infections, wound complications, or reoperations.Conclusion: Direct posterior endoscopic decompression for multilevel OPLL is feasible in symptomatic patients when executed at a high skill level. While 2-year outcomes were encouraging and on par with historic data obtained with traditional laminectomy, future studies will need to show whether any long-term shortcomings exist.

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Section: Discussionmentioning

confidence: 99%

Endoscopic Posterior Cervical Decompression for Ossified Posterior Longitudinal Ligament: A Technical Note

Zhang

Yan

et al. 2023

Int J Spine Surg

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A deep learning approach for cervical cord injury severity determination through axial and sagittal magnetic resonance imaging segmentation and classification

Artha Wiguna,

Kristian,

Deslivia

et al. 2024

Eur Spine J

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A Deep Learning Approach for Spine Cervical Injury Severity Determination through Axial and Sagittal Magnetic Resonance Imaging Segmentation and Classification

Wiguna,

Kristian,

Deslivia

et al. 2023

Preprint

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Objectives: Spinal cord injuries (SCI) require extensive efforts to predict the outcome of patients. While the ASIA Impairment Scale is the gold standard to assess patients with SCI, it has some limitations due to the subjectivity and impracticality in certain cases. Recent advances in machine learning (ML) and image recognition have prompted research into using these tools to predict outcomes. The aim of this study is to present a comprehensive analysis using deep learning techniques to evaluate and predict cervical spine injuries from MRI scans. Materials & Method: This is a cross-sectional database study, with patients admitted with traumatic and nontraumatic cervical SCI from 2019 to 2022 were included in our study. MRI images were labelled by four senior resident physicians. We trained a deep convolutional neural network using axial and sagittal cervical MRI images from our dataset and assessed model performance. Result: In the axial spinal cord segmentation, we achieved a dice score of 0.94 for and IoU score of 0.89. In the sagittal spinal cord segmentation, we obtained a dice scores up to 0.9201 and IoU scores up to 0.8541. The model for axial image score classification gave a satisfactory result with an f1 score of 0.72 and AUC of 0.79. Conclusion: Deep learning has been used in automated diagnostic tools, showing promise for significant future advancement. Our models were effectively able to identify cervical spinal cord injury on T2-weighted MR images with satisfactory performance. Further research is necessary to create an even more advanced model for predicting patient outcomes in spinal cord injury cases.

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Degenerative Cervical Myelopathy and Spinal Cord Injury: Introduction to the Special Issue

Abstract: Damage to the spinal cord (SC) can arise from either traumatic or non-traumatic spinal cord injury (SCI) [...]

Cited by 3 publications

References 17 publications

Endoscopic Posterior Cervical Decompression for Ossified Posterior Longitudinal Ligament: A Technical Note

Endoscopic Posterior Cervical Decompression for Ossified Posterior Longitudinal Ligament: A Technical Note

A deep learning approach for cervical cord injury severity determination through axial and sagittal magnetic resonance imaging segmentation and classification

A Deep Learning Approach for Spine Cervical Injury Severity Determination through Axial and Sagittal Magnetic Resonance Imaging Segmentation and Classification

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