STUDY DESIGN: Comprehensive literature review. PURPOSE: To document the criteria for fusion utilized in these studies to determine if a consensus on the definition of a solid fusion exists. OVERVIEW OF LITERATURE: Numerous studies have reported on fusion rates following anterior cervical arthrodesis. There is a wide discrepancy in the fusion rates in these studies. While factors such as graft type, Instrumentation, and technique play a factor in fusion rate, another reason for the difference may be a result of differences in the definition of fusion following anterior cervical spine surgery. METHODS: A comprehensive English Medline literature review from 1966 to 2004 using the key words "anterior," "cervical," and "fusion" was performed. We divided these into two groups: newer studies done between 2000 and 2004, and earlier studies done between 1966 and 2000. These articles were then analyzed for the number of patients, follow-up period, graft type, and levels fused. Moreover, all of the articles were examined for their definition of fusion along with their fusion rate. RESULTS: In the earlier studies from 1966 to 2000, there was no consensus for what constituted a solid fusion. Only fifteen percent of these studies employed the most stringent definition of a solid fusion which was the presence of bridging bone and the absence of motion on flexion and extension radiographs. On the other hand, the later studies (2000 to 2004) used such a definition a majority (63%) of the time, suggesting that a consensus opinion for the definition of fusion is beginning to form. CONCLUSIONS: Our study suggests that over the past several years, a consensus definition of fusion is beginning to form. However, a large percentage of studies are still being published without using stringent fusion criteria. To that end, we recommend that all studies reporting on fusion rates use the most stringent criteria for solid fusion following anterior cervical spine surgery: the absence of motion on flexion/extension views and presence of bridging trabeculae on lateral x-rays. We believe that a universal adoption of such uniform criteria will help to standardize such studies and make it more possible to compare one study with another.
Adoption ; Arthrodesis ; Collodion ; Consensus ; Follow-Up Studies ; Hand ; Humans ; Pseudarthrosis ; Spine ; Transplants

Objective: To develop and evaluate a technique using convolutional neural networks (CNNs) for the computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. By leveraging deep learning techniques, the study might potentially lead to improved patient outcomes and clinical decision-making. Methods: This study obtained 500 lateral radiographic cervical spine x-ray images from standard open-source dataset repositories to develop a classification model using CNNs. All the images contained diagnostic information, including normal cervical radiographic images (n=250) and fracture images of the cervical spine fracture (n=250). The model would classify whether the patient had a cervical spine fracture or not. Seventy percent of the images were training data sets used for model training, and 30% were for testing. Konstanz Information Miner (KNIME)’s graphic user interface-based programming enabled class label annotation, data preprocessing, CNNs model training, and performance evaluation. Results: The performance evaluation of a model for detecting cervical spine fractures presents compelling results across various metrics. This model exhibits high sensitivity (recall) values of 0.886 for fractures and 0.957 for normal cases, indicating its proficiency in identifying true positives. Precision values of 0.954 for fractures and 0.893 for normal cases highlight the model’s ability to minimize false positives. With specificity values of 0.957 for fractures and 0.886 for normal cases, the model effectively identifies true negatives. The overall accuracy of 92.14% highlights its reliability in correctly classifying cases by the area under the receiver operating characteristic curve. Conclusion We successfully used deep learning models for computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. This approach can assist the radiologist in screening, detecting, and diagnosing cervical spine fractures.

Objective: To develop and evaluate a technique using convolutional neural networks (CNNs) for the computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. By leveraging deep learning techniques, the study might potentially lead to improved patient outcomes and clinical decision-making. Methods: This study obtained 500 lateral radiographic cervical spine x-ray images from standard open-source dataset repositories to develop a classification model using CNNs. All the images contained diagnostic information, including normal cervical radiographic images (n=250) and fracture images of the cervical spine fracture (n=250). The model would classify whether the patient had a cervical spine fracture or not. Seventy percent of the images were training data sets used for model training, and 30% were for testing. Konstanz Information Miner (KNIME)’s graphic user interface-based programming enabled class label annotation, data preprocessing, CNNs model training, and performance evaluation. Results: The performance evaluation of a model for detecting cervical spine fractures presents compelling results across various metrics. This model exhibits high sensitivity (recall) values of 0.886 for fractures and 0.957 for normal cases, indicating its proficiency in identifying true positives. Precision values of 0.954 for fractures and 0.893 for normal cases highlight the model’s ability to minimize false positives. With specificity values of 0.957 for fractures and 0.886 for normal cases, the model effectively identifies true negatives. The overall accuracy of 92.14% highlights its reliability in correctly classifying cases by the area under the receiver operating characteristic curve. Conclusion We successfully used deep learning models for computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. This approach can assist the radiologist in screening, detecting, and diagnosing cervical spine fractures.

Objective: To develop and evaluate a technique using convolutional neural networks (CNNs) for the computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. By leveraging deep learning techniques, the study might potentially lead to improved patient outcomes and clinical decision-making. Methods: This study obtained 500 lateral radiographic cervical spine x-ray images from standard open-source dataset repositories to develop a classification model using CNNs. All the images contained diagnostic information, including normal cervical radiographic images (n=250) and fracture images of the cervical spine fracture (n=250). The model would classify whether the patient had a cervical spine fracture or not. Seventy percent of the images were training data sets used for model training, and 30% were for testing. Konstanz Information Miner (KNIME)’s graphic user interface-based programming enabled class label annotation, data preprocessing, CNNs model training, and performance evaluation. Results: The performance evaluation of a model for detecting cervical spine fractures presents compelling results across various metrics. This model exhibits high sensitivity (recall) values of 0.886 for fractures and 0.957 for normal cases, indicating its proficiency in identifying true positives. Precision values of 0.954 for fractures and 0.893 for normal cases highlight the model’s ability to minimize false positives. With specificity values of 0.957 for fractures and 0.886 for normal cases, the model effectively identifies true negatives. The overall accuracy of 92.14% highlights its reliability in correctly classifying cases by the area under the receiver operating characteristic curve. Conclusion We successfully used deep learning models for computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. This approach can assist the radiologist in screening, detecting, and diagnosing cervical spine fractures.

Objective: To develop and evaluate a technique using convolutional neural networks (CNNs) for the computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. By leveraging deep learning techniques, the study might potentially lead to improved patient outcomes and clinical decision-making. Methods: This study obtained 500 lateral radiographic cervical spine x-ray images from standard open-source dataset repositories to develop a classification model using CNNs. All the images contained diagnostic information, including normal cervical radiographic images (n=250) and fracture images of the cervical spine fracture (n=250). The model would classify whether the patient had a cervical spine fracture or not. Seventy percent of the images were training data sets used for model training, and 30% were for testing. Konstanz Information Miner (KNIME)’s graphic user interface-based programming enabled class label annotation, data preprocessing, CNNs model training, and performance evaluation. Results: The performance evaluation of a model for detecting cervical spine fractures presents compelling results across various metrics. This model exhibits high sensitivity (recall) values of 0.886 for fractures and 0.957 for normal cases, indicating its proficiency in identifying true positives. Precision values of 0.954 for fractures and 0.893 for normal cases highlight the model’s ability to minimize false positives. With specificity values of 0.957 for fractures and 0.886 for normal cases, the model effectively identifies true negatives. The overall accuracy of 92.14% highlights its reliability in correctly classifying cases by the area under the receiver operating characteristic curve. Conclusion We successfully used deep learning models for computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. This approach can assist the radiologist in screening, detecting, and diagnosing cervical spine fractures.

Objective: To develop and evaluate a technique using convolutional neural networks (CNNs) for the computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. By leveraging deep learning techniques, the study might potentially lead to improved patient outcomes and clinical decision-making. Methods: This study obtained 500 lateral radiographic cervical spine x-ray images from standard open-source dataset repositories to develop a classification model using CNNs. All the images contained diagnostic information, including normal cervical radiographic images (n=250) and fracture images of the cervical spine fracture (n=250). The model would classify whether the patient had a cervical spine fracture or not. Seventy percent of the images were training data sets used for model training, and 30% were for testing. Konstanz Information Miner (KNIME)’s graphic user interface-based programming enabled class label annotation, data preprocessing, CNNs model training, and performance evaluation. Results: The performance evaluation of a model for detecting cervical spine fractures presents compelling results across various metrics. This model exhibits high sensitivity (recall) values of 0.886 for fractures and 0.957 for normal cases, indicating its proficiency in identifying true positives. Precision values of 0.954 for fractures and 0.893 for normal cases highlight the model’s ability to minimize false positives. With specificity values of 0.957 for fractures and 0.886 for normal cases, the model effectively identifies true negatives. The overall accuracy of 92.14% highlights its reliability in correctly classifying cases by the area under the receiver operating characteristic curve. Conclusion We successfully used deep learning models for computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. This approach can assist the radiologist in screening, detecting, and diagnosing cervical spine fractures.

BACKGROUND: One of the characteristics of spinal stenosis is elastin degradation and fibrosis of the extracellular matrix of the ligamentum flavum. However, there have been no investigations to determine which biochemical factors cause these histologic changes. So we performed the current study to investigate the hypothesis that matrix metalloproteinases (MMPs), which possess the ability to cause extracellular matrix remodeling, may play a role as a mediator for this malady in the ligamentum flavum. METHODS: The ligamentum flavum specimens were surgically obtained from thirty patients with spinal stenosis, as well as from 30 control patients with a disc herniation. The extents of ligamentum flavum elastin degradation and fibrosis were graded (grade 0-4) with performing hematoxylin-eosin staining and Masson's trichrome staining, respectively. The localization of MMP-2 (gelatinase), MMP-3 (stromelysin) and MMP-13 (collagenase) within the ligamentum flavum tissue was determined by immunohistochemistry. The expressions of the active forms of MMP-2, MMP-3 and MMP-13 were determined by western blot analysis, and the blots were quantified using an imaging densitometer. The histologic and biochemical results were compared between the two conditions. RESULTS: Elastin degradation and fibrosis of the ligamentum flavum were significantly more severe in the spinal stenosis samples than that in the disc herniation samples (3.14 +/- 0.50 vs. 0.55 +/- 0.60, p < 0.001; 3.10 +/- 0.57 vs. 0.76 +/- 0.52, p < 0.001, respectively). The expressions of the active form of MMPs were identified in all the ligamentum flavums of the spinal stenosis and disc herniation patients. The expressions of active MMP-2 and MMP-13 were significantly higher in the spinal stenosis samples than that in the disc herniation samples (both p < 0.05). The expression of active MMP-3 was slightly higher in the spinal stenosis samples than that in the disc herniation samples, but the difference was not statistically significant (p = 0.131). MMP-2, -3, and -13 were positively stained on the ligamentum flavum fibroblasts. CONCLUSIONS: The current results suggest that the increased expression of active MMPs by the ligamentum flavum fibroblasts might be related to the elastin degradation and fibrosis of the ligamentum flavum in the patients who suffer with lumbar spinal stenosis.
Aged ; Blotting, Western ; Elastin/*metabolism ; Extracellular Matrix/metabolism/pathology ; Female ; Fibrosis ; Humans ; Immunohistochemistry ; Ligamentum Flavum/*metabolism/pathology ; *Lumbar Vertebrae ; Male ; Matrix Metalloproteinase 13/metabolism ; Matrix Metalloproteinase 2/metabolism ; Matrix Metalloproteinase 3/metabolism ; Matrix Metalloproteinases/*metabolism ; Middle Aged ; Spinal Stenosis/*metabolism/pathology

STUDY DESIGN: Retrospective review of the results of somatosensory evoked potentials (SSEP) performed in cervical spine surgery. PURPOSE: To evaluate the utility of spinal cord monitoring during cervical spine surgery in a single surgeon's practice, based on how often it prompted an intraoperative intervention. OVERVIEW OF LITERATURE: Intraoperative monitoring during cervical spine surgery is not a universally accepted standard of care. This is due in part to the paucity of literature regarding the impact of monitoring on patient management or outcome. METHODS: SSEP for tibial, median, and ulnar nerves were monitored in 809 consecutive cervical spine operations performed by a single surgeon. The average patient age was 52 years (range, 2 to 88 years), with 472 males and 339 females. Cases were screened for significant degradation or loss of SSEP data. Specific attention was paid to 1) what interventions were performed in response to the SSEP degradation with subsequent improvement, and 2) whether SSEP changes corresponded with postoperative neurological deficits. RESULTS: Seventeen of 809 patients (2.1%) had SSEP degradation that met warning criteria and therefore prompted intervention. Release of shoulder tape (8) or traction (4) most often resulted in SSEP improvement. Failure of SSEP data to return to within acceptable limits of baseline was associated with neurological deficit (p=0.04). Two patients awoke with new postoperative neurological deficits, which resolved in 6 hours and 2 months respectively. Patients with ossification of the posterior longitudinal ligament (OPLL) were at seven-fold greater risk of intraoperative SSEP degradation. CONCLUSIONS: SSEP monitoring in this surgical population proved sensitive to perioperative factors which may increase the risk of postoperative neurologic deficit, and probably prevented neurological deficits in 15 of 809 patients (1.9%). Improvement in data following intervention appears to correlate well with unchanged neurologic status. Experience with intraoperative monitoring in this patient series has led to incorporation of these techniques as a standard of care in cervical spine surgeries performed by this surgeon.
Evoked Potentials, Somatosensory* ; Female ; Humans ; Longitudinal Ligaments ; Male ; Monitoring, Intraoperative ; Neurologic Manifestations ; Retrospective Studies ; Shoulder ; Spinal Cord ; Spine* ; Standard of Care ; Traction ; Ulnar Nerve

Purpose: To analyze the cytokines that appear after a spinal cord injury in rats and to determine the agonists that regulate apoptosis. Materials and Methods: Sixty female Sprague-Dawley rats were anesthetized, and a laminectomy was performed at the 9th thoracic vertebra. The spinal cord injury was induced by dropping a 10 gm weight at a height of 20 mm. The subjects were divided into 5 groups. Group I was administered aminoguanidine, group II was administered GM-CSF, group III was administered riluzole, group IV was administered erythropoietin, and group V was administered methylprednisolone. A control group was administered normal saline. The results were assessed using the Tarlov motor grading method. In 1, 3, 5 and 7 days after the spinal cord injury, the rats were sacrificed and evaluated using the syringomyelic cavity size. Immunohistochemical staining for e-NOS and RT-PCR for XIAP were also performed. Results: Groups I, III, and V showed significantly different results in terms of the motor recovery and inhibition of increase in the syringomyelic cavity compared with the other groups (p<0.05). The e-NOS activity was observed in groups I, III, and V. The other groups showed almost no e-NOS activity. On the RT-PCR, groups I, III, and V showed significantly different results in terms of XIAP expression with time compared with the other groups. Conclusion: Steroids, NOS inhibitors and sodium channel inhibitors appear to be important factors for regulating apoptosis in the early stage of a spinal cord injury. Further study will be needed to develop new pharmaceuticals with synergic effects on spinal cord injuries.
Animals ; Apoptosis ; Cytokines ; Erythropoietin ; Female ; Granulocyte-Macrophage Colony-Stimulating Factor ; Humans ; Laminectomy ; Methylprednisolone ; Rats* ; Rats, Sprague-Dawley ; Riluzole ; Sodium Channel Blockers ; Spinal Cord Injuries ; Spine ; Steroids

The present systematic review and meta-analysis was conducted to compare the safety and efficacy of the two approaches for primary spinal cord tumors (PSCTs) in adult patients (laminoplasty [LP] vs. laminectomy [LE]). LE is one of the most common procedures for PSCTs. Despite advantages of LP, it is not yet widely used in the neurosurgical community worldwide. The efficacy of LP vs. LE remains controversial. Adult patients over 18 years of age with PSCT at the level of the cervical, thoracic, and lumbar spine were included in the study. A literature search was performed in MEDLINE via PubMed, EMBASE, The Cochrane Library, and Google Scholar up to December 2021. Operation time, hospital stay, complications, and incidence of postoperative spinal deformity (kyphosis or scoliosis were extracted. A total of seven retrospective observational studies with 540 patients were included. There were no significant differences between LP and LE group in operation time (p =0.25) and complications (p =0.48). The LE group showed larger postoperative spinal deformity rate than the LP group (odds ratio, 0.47; 95% confidence interval [CI], 0.27−0.84; p =0.01). The LP group had a shorter hospital stay (standardized mean differences, −0.68; 95% CI, −1.03 to −0.34; p =0.0001) than the LE group. Both LP and LE have comparable operative times and total complications in the treatment of PSCT. LP was superior to LE in hospital stay and postoperative spinal deformity rate. However, these findings are limited by the very low quality of the available evidence. Randomized controlled trials are needed for further comparison.