Methods: This study analyzed 432 open-mouth (odontoid) radiographic views of cervical spine X-ray images obtained from dataset repositories, which were used in developing ANN models based on the convolutional neural network theory. All the images contained diagnostic information, including 216 radiographic images of individuals with normal odontoid processes and 216 images of patients with acute odontoid fractures. The model classified each image as either showing an odontoid fracture or not. Specifically, 70% of the images were training datasets used for model training, and 30% were used for testing. KNIME’s graphic user interface-based programming enabled class label annotation, data preprocessing, model training, and performance evaluation. Results: The graphic user interface program by KNIME was used to report all radiographic X-ray imaging features. The ANN model performed 50 epochs of training. The performance indices in detecting odontoid fractures included sensitivity, specificity, F-measure, and prediction error of 100%, 95.4%, 97.77%, and 2.3%, respectively. The model’s accuracy accounted for 97% of the area under the receiver operating characteristic curve for the diagnosis of odontoid fractures. Conclusions The ANN models with the KNIME Analytics Platform were successfully used in the computer-assisted diagnosis of odontoid fractures using radiographic X-ray images. This approach can help radiologists in the screening, detection, and diagnosis of acute odontoid fractures.

Methods: Data from patients who underwent minimally invasive (MI) fusion surgery for lumbar degenerative diseases at L4–L5 level was analyzed. Thirty patients each from MIS-TLIF, XLIF, and OLIF groups were recruited for propensity score matching. Visual Analog Scale (VAS) of the back and legs and Oswestry Disability Index (ODI) were evaluated preoperatively and at 1, 3, and 6 months and 1 year postoperatively. Radiographic outcomes were also compared. The fusion rate was evaluated at 1 year after surgeries. Results: The clinical outcomes were significantly improved in all groups. The disk height was significantly restored in all groups postoperatively, which was significantly more improved in XLIF and OLIF than MIS-TLIF group (p<0.001). The axial canal area was significantly increased more in MIS-TLIF versus XLIF and OLIF (p<0.001). The correction of lumbar lordotic angle and segmental sagittal angle were similar among these techniques. OLIF and XLIF groups showed less blood loss and shorter hospital stays than MIS-TLIF group (p<0.001). There was no significant difference in fusion rate among all groups. Conclusions MIS-TLIF, XLIF, and OLIF facilitated safe and effective MI procedures for treating lumbar degenerative diseases. XLIF and OLIF can achieve clinical outcomes equivalent to MIS-TLIF by indirect decompression. XLIF and OLIF showed less blood loss, shorter hospital stays, and better disk and foraminal height restorations. In single-level L4–5, the restoration of sagittal alignment was similar between these three techniques.