OBJECTIVE:Anterior cervical decompression and fusion is a classic surgical method for the treatment of degenerative cervical spondylosis.The use of nail plates increases the fusion rate and stability and indirectly leads to adjacent vertebral degeneration and postoperative dysphagia.In this paper,the clinical results and complications of ROI-CTM self-locking system and traditional cage combined with screw-plate internal fixation in the treatment of degenerative cervical spondylosis were compared by meta-analysis to provide evidence-based support for the selection of internal fixation methods in anterior cervical decompression and fusion. METHODS:CNKI,WanFang,VIP,PubMed,Cochrane Library,Web of Science,and Embase databases were searched for Chinese and English literature on the application of ROI-CTM self-locking system and fusion cage combined with screw plate internal fixation in the treatment of degenerative cervical spondylosis.The retrieval time range was from inception to July 2023.Two researchers selected the literature strictly according to the inclusion and exclusion criteria.The Cochrane bias risk tool was used to evaluate the quality of randomized controlled trials.Newcastle-Ottawa Scale was used to assess the quality of cohort studies.Meta-analysis was performed using RevMan 5.4 software.Outcome indicators included operation time,intraoperative blood loss,Japanese Orthopaedic Association score,Neck Disability Index,C2-C7 Cobb angle,fusion rate,incidence of adjacent vertebral degeneration,cage subsidence rate,and incidence of dysphagia. RESULTS:Thirteen articles were included,including eleven retrospective cohort studies and two randomized controlled trials,with 1 136 patients,569 in the ROI-C group,and 567 in the cage combined with the nail plate group.Meta-analysis results showed that the operation time(MD=-15.52,95%CI:-18.62 to-12.42,P<0.000 01)and intraoperative blood loss(MD=-24.53,95%CI:-32.46 to-16.61,P<0.000 01)in the ROI-C group and the fusion device combined with nail plate group.Postoperative adjacent segment degeneration rate(RR=0.40,95%CI:0.27-0.60,P<0.000 01)and postoperative total dysphagia rate(RR=0.18,95%CI:0.13-0.26),P<0.000 01)were statistically different.The two groups had no significant difference in Japanese Orthopaedic Association score,Neck Disability Index,C2-C7 Cobb angle,fusion rate,or cage subsidence rate(P≥0.05). CONCLUSION:Applying an ROI-CTM self-locking system and traditional cage combined with plate internal fixation in anterior cervical decompression and fusion can achieve satisfactory clinical results in treating degenerative cervical spondylosis.The operation of the ROI-CTM self-locking system is more straightforward.Compared with a cage combined with plate internal fixation,the ROI-CTM self-locking system can significantly reduce the operation time and intraoperative blood loss and has obvious advantages in reducing the incidence of postoperative dysphagia and adjacent segment degeneration.The ROI-CTM self-locking system is recommended for patients with skip cervical spondylosis and adjacent vertebral disease.However,given its possible high settlement rate,using a fusion cage combined with screw-plate internal fixation is still recommended for patients with degenerative cervical spondylosis with multiple segments and high-risk factors of fusion cage settlement,such as osteoporosis and vertebral endplate damage.

Coronavirus disease 2019 (COVID-19) has spread rapidly around the world. In order to diagnose COVID-19 more quickly, in this paper, a depthwise separable DenseNet was proposed. The paper constructed a deep learning model with 2 905 chest X-ray images as experimental dataset. In order to enhance the contrast, the contrast limited adaptive histogram equalization (CLAHE) algorithm was used to preprocess the X-ray image before network training, then the images were put into the training network and the parameters of the network were adjusted to the optimal. Meanwhile, Leaky ReLU was selected as the activation function. VGG16, ResNet18, ResNet34, DenseNet121 and SDenseNet models were used to compare with the model proposed in this paper. Compared with ResNet34, the proposed classification model of pneumonia had improved 2.0%, 2.3% and 1.5% in accuracy, sensitivity and specificity respectively. Compared with the SDenseNet network without depthwise separable convolution, number of parameters of the proposed model was reduced by 43.9%, but the classification effect did not decrease. It can be found that the proposed DWSDenseNet has a good classification effect on the COVID-19 chest X-ray images dataset. Under the condition of ensuring the accuracy as much as possible, the depthwise separable convolution can effectively reduce number of parameters of the model.
Betacoronavirus ; Coronavirus Infections ; diagnostic imaging ; Deep Learning ; Humans ; Pandemics ; Pneumonia, Viral ; diagnostic imaging ; X-Rays