Therapeutic study, Level I.

Introduction: Comparison was made of the clinical and radiological results of the surgical treatments of proximal femoral nail (PFN), dynamic hip screw (DHS) or proximal femoral locking compression plate (PF-LCP) in patients with AO 31A2.2/2.3 unstable intertrochanteric femoral fracture(ITF). Methods: Evaluation was made of a total of 91 patients in respect of age, gender, time from fracture to surgery, operating time, amount of blood replacement, total hospitalisation, follow-up period, time to full weight-bearing, time to union, complications and Harris hip scores(HHS). Results: A statistically significant difference was determined between the groups in respect of perioperative operating time, blood replacement and hospitalisation period with the values of the PFN group seen to be superior to those of the other two groups (p < 0.001). No significant difference was determined beween the DHS and PFN groups in respect of time to union and in the long-term HHS, both groups were seen to be superior to the PF-LCP group (p < 0.001). Full weight-bearing was statistically significantly earlier in the PFN group (p < 0.001). The numbers of implant failures was statistically significantly higher in the PF-LCP group (p < 0.001). Conclusion: The new generation intra-medullar nails are easy to apply and have more successful clinical results compared to extra-medullar implants in the treatment of A2 unstable ITF. Due to the high rates of implant failure, PF-LCP should not be preferred in these fractures.

therapeutic study (retrospective comparative study), Level III.

This study evaluated complications associated with implant depth in headless compression screw treatment of an osteochondral fracture associated with a traumatic patellar dislocation in a 21-year-old woman. Computed tomography and X-rays showed one lateral fracture fragment measuring 25 × 16 mm. Osteosynthesis was performed with two headless compression screws. Five months later, the screws were removed because of patella-femoral implant friction. We recommend that the screw heads be embedded to a depth of at least 3 mm below the cartilage surface. Further clinical studies need to examine the variation in cartilage thickness in the fracture fragment.

Purpose: Machine-learning algorithms are a subset of artificial intelligence that have proven to enhance analytics in medicine across various platforms. Spine surgery has the potential to benefit from improved hardware placement utilizing algorithms that autonomously and accurately measure pedicle and vertebral body anatomy. The purpose of this study was to assess the accuracy of an autonomous convolutional neural network (CNN) in measuring vertebral body anatomy utilizing clinical lumbar computed tomography (CT) scans and automatically segment vertebral body anatomy. Methods: The CNN was trained utilizing 8000 manually segmented CT slices from 15 cadaveric specimens and 30 adult diagnostic scans. Validation was performed with twenty randomly selected patient datasets. Anatomic landmarks that were segmented included the pedicle, vertebral body, spinous process, transverse process, facet joint, and lamina. Morphometric measurement of the vertebral body was compared between manual measurements and automatic measurements. Results: Automatic segmentation was found to have a mean accuracy ranging from 96.38% to 98.96%. Coaxial distance from the lamina to the anterior cortex was 99.10% with pedicle angulation error of 3.47%. Conclusion: The CNN algorithm tested in this study provides an accurate means to automatically identify the vertebral body anatomy and provide measurements for implants and placement trajectories.