OBJECTIVE: To establish the mode of anterior cervical surgery in outpatient setting, and evaluate its preliminary effectiveness. METHODS: A clinical data of patients who underwent anterior cervical surgery between January 2022 and September 2022 and met the selection criteria was retrospectively analyzed. The surgeries were performed in outpatient setting ( n=35, outpatient setting group) or in inpatient setting ( n=35, inpatient setting group). There was no significant difference between the two groups ( P>0.05) in age, gender, body mass index, smoking, history of alcohol drinking, disease type, the number of surgical levels, operation mode, as well as preoperative Japanese Orthopaedic Association (JOA) score, visual analogue scale score of neck pain (VAS-neck), and visual analogue scale score of upper limb pain (VAS-arm). The operation time, intraoperative blood loss, total hospital stay, postoperative hospital stay, and hospital expenses of the two groups were recorded; JOA score, VAS-neck score, and VAS-arm score were recorded before and immediately after operation, and the differences of the above indexes between pre- and post-operation were calculated. Before discharge, the patient was asked to score satisfaction with a score of 1-10. RESULTS: The total hospital stay, postoperative hospital stay, and hospital expenses were significantly lower in the outpatient setting group than in the inpatient setting group ( P<0.05). The satisfaction of patients was significantly higher in the outpatient setting group than in the inpatient setting group ( P<0.05). There was no significant difference between the two groups in operation time and intraoperative blood loss ( P>0.05). The JOA score, VAS-neck score, and VAS-arm score of the two groups significantly improved at immediate after operation when compared with those before operation ( P<0.05). There was no significant difference in the improvement of the above scores between the two groups ( P>0.05). The patients were followed up (6.67±1.04) months in the outpatient setting group and (5.95±1.90) months in the inpatient setting group, with no significant difference ( t=0.089, P=0.929). No surgical complications, such as delayed hematoma, delayed infection, delayed neurological damage, and esophageal fistula, occurred in the two groups. CONCLUSION The safety and efficiency of anterior cervical surgery performed in outpatient setting were comparable to that performed in inpatient setting. Outpatient surgery mode can significantly shorten the postoperative hospital stay, reduce hospital expenses, and improve the patients' medical experience. The key points of the outpatient mode of anterior cervical surgery are minimizing damage, complete hemostasis, no drainage placement, and fine perioperative management.
Humans ; Treatment Outcome ; Cervical Vertebrae/surgery* ; Outpatients ; Retrospective Studies ; Blood Loss, Surgical ; Spinal Fusion ; Neck Pain

Objective To propose two novel methods based on deep learning for computer-aided tongue diagnosis, including tongue image segmentation and tongue color classification, improving their diagnostic accuracy. Methods LabelMe was used to label the tongue mask and Snake model to optimize the labeling results. A new dataset was constructed for tongue image segmentation. Tongue color was marked to build a classified dataset for network training. In this research, the Inception + Atrous Spatial Pyramid Pooling (ASPP) + UNet (IAUNet) method was proposed for tongue image segmentation, based on the existing UNet, Inception, and atrous convolution. Moreover, the Tongue Color Classification Net (TCCNet) was constructed with reference to ResNet, Inception, and Triple-Loss. Several important measurement indexes were selected to evaluate and compare the effects of the novel and existing methods for tongue segmentation and tongue color classification. IAUNet was compared with existing mainstream methods such as UNet and DeepLabV3+ for tongue segmentation. TCCNet for tongue color classification was compared with VGG16 and GoogLeNet. Results IAUNet can accurately segment the tongue from original images. The results showed that the Mean Intersection over Union (MIoU) of IAUNet reached 96.30%, and its Mean Pixel Accuracy (MPA), mean Average Precision (mAP), F1-Score, G-Score, and Area Under Curve (AUC) reached 97.86%, 99.18%, 96.71%, 96.82%, and 99.71%, respectively, suggesting IAUNet produced better segmentation than other methods, with fewer parameters. Triplet-Loss was applied in the proposed TCCNet to separate different embedded colors. The experiment yielded ideal results, with F1-Score and mAP of the TCCNet reached 88.86% and 93.49%, respectively. Conclusion IAUNet based on deep learning for tongue segmentation is better than traditional ones. IAUNet can not only produce ideal tongue segmentation, but have better effects than those of PSPNet, SegNet, UNet, and DeepLabV3+, the traditional networks. As for tongue color classification, the proposed network, TCCNet, had better F1-Score and mAP values as compared with other neural networks such as VGG16 and GoogLeNet.