Objective: Virtual and augmented reality have enjoyed increased attention in spine surgery. Preoperative planning, pedicle screw placement, and surgical training are among the most studied use cases. Identifying osseous structures is a key aspect of navigating a 3-dimensional virtual reconstruction. To automate the otherwise time-consuming process of labeling vertebrae on each slice individually, we propose a fully automated pipeline that automates segmentation on computed tomography (CT) and which can form the basis for further virtual or augmented reality application and radiomic analysis.Methods: Based on a large public dataset of annotated vertebral CT scans, we first trained a YOLOv8m (You-Only-Look-Once algorithm, Version 8 and size medium) to detect each vertebra individually. On the then cropped images, a 2D-U-Net was developed and externally validated on 2 different public datasets.Results: Two hundred fourteen CT scans (cervical, thoracic, or lumbar spine) were used for model training, and 40 scans were used for external validation. Vertebra recognition achieved a mAP50 (mean average precision with Jaccard threshold of 0.5) of over 0.84, and the segmentation algorithm attained a mean Dice score of 0.75 ± 0.14 at internal, 0.77 ± 0.12 and 0.82 ± 0.14 at external validation, respectively.Conclusion: We propose a 2-stage approach consisting of single vertebra labeling by an object detection algorithm followed by semantic segmentation. In our externally validated pilot study, we demonstrate robust performance for our object detection network in identifying individual vertebrae, as well as for our segmentation model in precisely delineating the bony structures.

Objective: Computed tomography (CT) imaging is a cornerstone in the assessment of patients with spinal trauma and in the planning of spinal interventions. However, CT studies are associated with logistical problems, acquisition costs, and radiation exposure. In this proof-of-concept study, the feasibility of generating synthetic spinal CT images using biplanar radiographs was explored. This could expand the potential applications of x-ray machines pre-, post-, and even intraoperatively.Methods: A cohort of 209 patients who underwent spinal CT imaging from the VerSe2020 dataset was used to train the algorithm. The model was subsequently evaluated using an internal and external validation set containing 55 from the VerSe2020 dataset and a subset of 56 images from the CTSpine1K dataset, respectively. Digitally reconstructed radiographs served as input for training and evaluation of the 2-dimensional (2D)-to-3-dimentional (3D) generative adversarial model. Model performance was assessed using peak signal to noise ratio (PSNR), structural similarity index (SSIM), and cosine similarity (CS).Results: At external validation, the developed model achieved a PSNR of 21.139 ± 1.018 dB (mean ± standard deviation). The SSIM and CS amounted to 0.947 ± 0.010 and 0.671 ± 0.691, respectively.Conclusion: Generating an artificial 3D output from 2D imaging is challenging, especially for spinal imaging, where x-rays are known to deliver insufficient information frequently. Although the synthetic CT scans derived from our model do not perfectly match their ground truth CT, our proof-of-concept study warrants further exploration of the potential of this technology.

Aims: To determine the major risk factors for unplanned reoperations (UROs) following corrective surgery for adult spinal deformity (ASD) and their interactions, using machine learning-based prediction algorithms and game theory.Methods: Patients who underwent surgery for ASD, with a minimum of two-year follow-up, were retrospectively reviewed. In total, 210 patients were included and randomly allocated into training (70% of the sample size) and test (the remaining 30%) sets to develop the machine learning algorithm. Risk factors were included in the analysis, along with clinical characteristics and parameters acquired through diagnostic radiology.Results: Overall, 152 patients without and 58 with a history of surgical revision following surgery for ASD were observed; the mean age was 68.9 years (SD 8.7) and 66.9 years (SD 6.6), respectively. On implementing a random forest model, the classification of URO events resulted in a balanced accuracy of 86.8%. Among machine learning-extracted risk factors, URO, proximal junction failure (PJF), and postoperative distance from the posterosuperior corner of C7 and the vertical axis from the centroid of C2 (SVA) were significant upon Kaplan-Meier survival analysis.Conclusion: The major risk factors for URO following surgery for ASD, i.e. postoperative SVA and PJF, and their interactions were identified using a machine learning algorithm and game theory. Clinical benefits will depend on patient risk profiles.Cite this article: Bone Joint Res 2023;12(4):245–255.

Objective: The risks of nonunion and subsidence are high in patients with bone density loss undergoing spinal fusion surgery. The internal application of recombinant human bone morphogenic protein 2 (rhBMP-2) in an interbody cage improves spinal fusion; however, related complications have been reported. Denosumab, a human monoclonal antibody targeting the receptor activator of nuclear factor kappa B ligand (RANKL), hinders osteoblast differentiation and function. Therefore, this study aimed to observe the combined effect of the local application of rhBMP-2 in a lumbar cage and systemic RANKL inhibition on postoperative spinal fusion in patients with bone density loss undergoing posterior lumbar interbody fusion (PLIF).Methods: This retrospective observational study included 251 consecutive patients with spinal stenosis who underwent PLIF at a single center between 2017 and 2021. Clinical outcomes were assessed, and radiographic evaluations included lumbar flexion, extension, range of motion, and subsidence. Statistical analyses were conducted to identify the combined effect of the treatment and the subsidence and spinal fusion status.Results: One hundred patients were included in the final analysis. Denosumab treatment significantly reduced the rate of osteolysis (p = 0.013). When denosumab was administered in combination with rhBMP-2, the fusion status remained similar; however, the incidences of postoperative osteolysis and postoperative oozing day decreased.Conclusion: The combined use of rhBMP-2 and RANKL inhibition in patients with bone density loss can enhance bone formation after PLIF with fewer complications than rhBMP-2 alone.

Intracranial pressure (ICP) monitoring is desirable as a first-line measure to assist decision-making in cases of increased ICP. Clinically, non-invasive ICP monitoring is also required to avoid infection and hemorrhage in patients. The relationships among the arterial blood pressure (ABP), ICP, cerebral blood flow, and its velocity (QCBFv) measured by transcranial Doppler ultrasound measurement have been reported. However, real-time non-invasive ICP estimation using these modalities is less well documented. This paper presents a novel algorithm for real-time and non-invasive ICP monitoring with QCBFv and ABP, called direct-current (DC)-ICP. The technique was compared with invasive ICP for 10 acute-brain-injury patients admitted to Cheju Halla Hospital and Gangnam Severance Hospital from July 2017 to June 2018. The inter-subject correlation coefficient between true and estimate was 0.75 and the AUCs of the ROCs for prediction of increased ICP for the DC-ICP methods were 0.83. Thus, QCBFv monitoring can facilitate reliable real-time ICP tracking with our novel DC-ICP algorithm, which can provide valuable information under clinical conditions.

The atlantoaxial (C1–2) junction is among the most technically demanding regions for cervical spine surgery owing to its complex osseoligamentous anatomy and proximity to critical neurovascular structures. Numerous posterior fixation constructs have been developed to optimize biomechanical rigidity and promote arthrodesis. Since Gallie’s introduction of posterior wiring with autologous bone grafts in 1939, evolving techniques have focused on enhancing fusion rates while minimizing risk to adjacent structures. This paper outlines the historical evolution of C1–2 posterior instrumentation, current fixation strategies, bone fusion techniques, and reduction methods. A systematic literature search identified 61 relevant studies on C1–2 fusion. Additional references were manually reviewed to provide a comprehensive context. Of these, 41 studies were narratively summarized to outline the historical and conceptual evolution of C1–2 fusion techniques, while the remaining 20 post-2000 studies on contemporary surgical modifications were systematically reviewed and tabulated for technical details and clinical outcomes. C1–2 fusion techniques have evolved significantly over time. Early methods primarily involved posterior wiring with autologous bone grafts, but later transitioned to rigid segmental fixation using pedicle screw constructs, resulting in improved fusion rates and clinical outcomes. Interarticular fusion, when concurrently performed, enhances the biological fusion environment, contributing to favorable clinical results. C1 lateral mass, posterior arch, pedicle screws and C2 pedicle, lamina screws give us much stronger stability and higher fusion rates. Interarticular fusion using local bone also gives us technical easiness guaranteeing high fusion rate overcoming inconvenience of wiring and iliac bone harvest. Interarticular height reduction and interarticular fusion should be discriminated.