The aim of this study is to compare mTLIF vs. oTLIF with regard to peri-operative complications, operative time, estimated blood loss, fluoroscopic time, and the length of hospital stay.

Introduction: In light of the potential problems of missed diagnosis and misdiagnosis in the diagnosis of spinal diseases caused by experience differences and fatigue, this paper investigates the use of artificial intelligence technology for auxiliary diagnosis of spinal diseases.Methods: The LableImg tool was used to label the MRIs of 604 patients by clinically experienced doctors. Then, in order to select an appropriate object detection algorithm, deep transfer learning models of YOLOv3, YOLOv5, and PP-YOLOv2 were created and trained on the Baidu PaddlePaddle framework. The experimental results showed that the PP-YOLOv2 model achieved a 90.08% overall accuracy in the diagnosis of normal, IVD bulges and spondylolisthesis, which were 27.5 and 3.9% higher than YOLOv3 and YOLOv5, respectively. Finally, a visualization of the intelligent spine assistant diagnostic software based on the PP-YOLOv2 model was created and the software was made available to the doctors in the spine and osteopathic surgery at Guilin People's Hospital.Results and discussion: This software automatically provides auxiliary diagnoses in 14.5 s on a standard computer, is much faster than doctors in diagnosing human spines, which typically take 10 min, and its accuracy of 98% can be compared to that of experienced doctors in the comparison of various diagnostic methods. It significantly improves doctors' working efficiency, reduces the phenomenon of missed diagnoses and misdiagnoses, and demonstrates the efficacy of the developed intelligent spinal auxiliary diagnosis software.

Objectives: We retrospectively investigated the clinical materials to seek the factors that lead to relapse after using the Ponseti method. Methods: We retrospectively reviewed all children with congenital clubfoot treated with the Ponseti method in our hospital from June 2008 to June 2013. The data included the following factors: age, gender, initial Pinari score, number of casts, number of feet (unilateral or bilateral), age at the first casting, age of mother, tenotomy, walking age, and compliance with using bracing. All investigations were conducted in conformity with ethical standards. This study was approved by Guilin Peoples’ Hospital Ethics Committee. Results: In this study, there were 148 cases (164 ft) in total that underwent the Ponseti method, with the follow–up period at least 5 years. Of them, 64 children presented with left side, 58 with right side, and 26 with bilateral cases. This study included 75 males and 73 females; sex did not affect the outcomes. The mean age of the first casting was 2.50 ± 2.15 months. The average initial Pirani score was 4.98 ± 1.33, and the average number of casts was 5.71 ± 2.28 times. The mean age of mothers at birth was 25.81 ± 2.38 years old. The walking age of children was at a mean of 14.83 ± 1.18 months. Forty-nine cases could not tolerate using braces, namely the rate of noncompliance in this study was 33.1%. Tenotomy was performed on 113 ft (76.4%). The average follow–up period was 7.27 ± 1.29 years (from 5 to 10 years). The rate of relapse was 21.6% (32 cases) at the end of the follow-up. The rate of relapse in the noncompliance with using bracing group was significantly higher compared to the compliance group . Conclusion: The initial Pirani score, compliance with the foot abduction brace and the age at the first casting are three independent factors for relapse in clubfoot.

Background: To explore the correlation of the vertebral compression degree and cancellous bone CT HU in elderly patients with osteoporotic thoracolumbar fractures. Methods: Elderly patients with single-segment vertebral fragility fractures were retrospectively reviewed. All patients experienced a low-energy trauma and underwent thoracolumbar MRI. The consistency of measurement between two spine surgeons was evaluated. The average CT HU value of the adjacent vertebral body was used instead. Results: A total of 54 patients were included in the final analysis. The patients’ average age was 70.39 ± 8.53 years, and the average CT HU value was 72.78 ± 29.75 HU. The average vertebral compression ratio was 0.57 ± 0.16. Measurements showed both good intrarater repeatability and good interrater reproducibility of the vertebral compression ratio (ICC = 0.978). The degree of vertebral compression in thoracolumbar osteoporotic fractures was strongly positively correlated with the cancellous bone CT HU value (P < 0.01). Conclusions: The local bone quality as evaluated by the CT HU value is an important factor affecting the degree of compression in osteoporotic vertebral fractures. This study provides quantitative evidence that a greater compression ratio with thoracolumbar osteoporotic fractures was associated with lower bone density in elderly patients. Further longitudinal studies with larger cohorts are needed to verify this relationship.

Chondromyxoid fibroma (CMF) is an unusual benign tumour of cartilaginous tissues that may be confused with other malignant tumours. It is rarely seen in the cervical spine. A 24-year-old young woman was admitted to the hospital because of neck and shoulder pain. Computed tomography, magnetic resonance imaging, X-ray and other imaging examinations of the cervical spine and laboratory-related indicators combined with intraoperative pathology revealed that the patient had cervical CMF. We performed total resection of the vertebral body and intervertebral disc, and internal fixation was performed to simultaneously maintain the stability of the entire spine. The clinical results from extensive resection were satisfactory. At the 2-year follow-up, the patient's symptoms had not recurred. CMF is a benign primary bone tumour that is rarely located in the vertebral bone. Accurate initial diagnosis of these tumours is important for appropriate treatment. En bloc surgical resection of the tumour is the cornerstone of treatment.

To develop a nomogram model for the prediction of the risk of prolonged length of hospital stay (LOS) in spinal fusion patients. A retrospective cohort study was carried out on 6272 patients who had undergone spinal fusion surgery. Least absolute shrinkage and selection operator (LASSO) regression was performed on the training sets to screen variables, and the importance of independent variables was ranked via random forest. In addition, various independent variables were used in the construction of models 1 and 2. A receiver operating characteristic curve was used to evaluate the models’ predictive performance. We employed Delong tests to compare the area under the curve (AUC) of the different models. Assessment of the models’ capability to improve classification efficiency was achieved using continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI). The Hosmer–Lemeshow method and calibration curve was utilised to assess the calibration degree, and decision curve to evaluate its clinical practicality. A bootstrap technique that involved 10 cross-validations and was performed 10,000 times was used to conduct internal and external validation. The were outcomes of the model exhibited in a nomogram graphics. The developed nomogram was validated both internally and externally. Model 1 was identified as the optimal model. The risk factors for prolonged LOS comprised blood transfusion, operation type, use of tranexamic acid (TXA), diabetes, electrolyte disturbance, body mass index (BMI), surgical procedure performed, the number of preoperative diagnoses and operative time. The diagnostic performance of the nomogram model was satisfactory, with AUC values of 0.784 and 0.795 for the internal and external validation sets, respectively. Model discrimination was favourable in both the internal (C-statistic, 0.811) and external (C-statistic, 0.814) validation sets. Calibration curve and Hosmer-Lemeshow test showed acceptable agreement between predicted and actual results. The decision curve shows that the model provides net clinical benefit within a certain decision threshold range. This study developed and validated a nomogram to identify the risk of prolonged LOS in spinal fusion patients, which may help clinicians to identify high-risk groups at an early stage. Predictors identified included blood transfusion, operation type, use of TXA, diabetes, electrolyte disturbance, BMI, surgical procedure performed, number of preoperative diagnoses and operative time.