Facet joint violation (FJV) is a known complication of pedicle screw fixation that may contribute to adjacent segment disease (ASD) by altering spinal biomechanics and increasing loading of the cranial facet joints. However, the prevalence, biomechanical effects, and longterm consequences of FJV remain unclear. A scoping review was conducted using the PubMed, Embase, and Scopus databases. Clinical studies reporting the number of FJVs in relation to screws placed during lumbar fusion or assessing the frequency of ASD in patients with FJV were included. Biomechanical studies evaluating segmental kinematics and loading after FJV were also included. Data regarding study characteristics, surgical techniques, FJV rates, severity grading, and outcomes were extracted. Bayesian statistical models were applied for pooled prevalence estimates. Fifty studies met the inclusion criteria (39 clinical and 11 biomechanical studies). The prevalence of FJV varied, with robotic-assisted percutaneous placement associated with the lowest risk (4.79%; 95% credible interval [CrI], 3.88–5.79), and freehand percutaneous placement associated with the highest risk (19.45%; 95% CrI, 18.15–20.73). FJV rates were highest at L2 (14.5%; 95% CrI, 10.4%–19.0%) compared to lower levels. Biomechanical studies indicate that minor FJV may destabilize the superior segment by disrupting the facet capsule, while severe FJV involving full joint traversal might stabilize the segment due to the screw’s interaction with both articulating processes. Limited evidence suggests an association between FJV and ASD, though methodological limitations, selection bias, and reporting errors limit conclusions. FJV is common during lumbar fusion, especially with percutaneous screw placement. While biomechanical evidence suggests differing grades of FJV may variably alter segmental stability, its role in ASD development remains uncertain. Given its prevalence, biomechanical implications, and potential mitigation through navigation or robotics, determining whether FJV is a clinically significant driver of ASD is a key research imperative.

Computed tomography (CT) is widely used for the diagnosis and surgical treatment of spinal pathologies, particularly for pedicle screw placement. However, CT’s limitations, notably radiation exposure, necessitate the development of alternative imaging techniques. Synthetic CT (sCT), which generates CT-like images from existing magnetic resonance imaging (MRI) scans, offers a promising alternative to reduce radiation exposure. This study examines the emerging role of sCT in spinal surgery, focusing on usability, efficiency, and potential impact on surgical outcomes. This qualitative literature review evaluated various sCT generation methods, encompassing traditional atlas-based and bulk-density models, as well as advanced convolutional neural network (CNN) architectures, including U-net, V-net, and generative adversarial network models. The review assessed sCT accuracy and clinical feasibility across different medical disciplines, particularly oncology and surgery, with potential applications in orthopedic, neurosurgical, and spinal surgery. sCT has shown significant promise across various medical disciplines. CNN-based techniques enable rapid and accurate generation of sCT from MRI scans, rendering clinical use feasible. sCT has been used to identify pathologies and monitor disease progression, suggesting that MRI alone may suffice for diagnosis and planning in the future. In spinal surgery, sCTs are particularly useful in visualizing key anatomical features like vertebral dimensions and spinal canal diameter. However, challenges persist, especially in visualizing complex structures and larger spinal regions, like the lumbar spine. Additional limitations include inaccuracies stemming from surgical implants and image variability. The application of sCT technology in spinal surgery holds great promise, improving diagnostics, planning, and treatment outcomes. Although further research is required to improve its precision, it offers a viable alternative to traditional CT in many clinical contexts, with the potential for broader application as the technology matures.

Computed tomography (CT) is widely used for the diagnosis and surgical treatment of spinal pathologies, particularly for pedicle screw placement. However, CT’s limitations, notably radiation exposure, necessitate the development of alternative imaging techniques. Synthetic CT (sCT), which generates CT-like images from existing magnetic resonance imaging (MRI) scans, offers a promising alternative to reduce radiation exposure. This study examines the emerging role of sCT in spinal surgery, focusing on usability, efficiency, and potential impact on surgical outcomes. This qualitative literature review evaluated various sCT generation methods, encompassing traditional atlas-based and bulk-density models, as well as advanced convolutional neural network (CNN) architectures, including U-net, V-net, and generative adversarial network models. The review assessed sCT accuracy and clinical feasibility across different medical disciplines, particularly oncology and surgery, with potential applications in orthopedic, neurosurgical, and spinal surgery. sCT has shown significant promise across various medical disciplines. CNN-based techniques enable rapid and accurate generation of sCT from MRI scans, rendering clinical use feasible. sCT has been used to identify pathologies and monitor disease progression, suggesting that MRI alone may suffice for diagnosis and planning in the future. In spinal surgery, sCTs are particularly useful in visualizing key anatomical features like vertebral dimensions and spinal canal diameter. However, challenges persist, especially in visualizing complex structures and larger spinal regions, like the lumbar spine. Additional limitations include inaccuracies stemming from surgical implants and image variability. The application of sCT technology in spinal surgery holds great promise, improving diagnostics, planning, and treatment outcomes. Although further research is required to improve its precision, it offers a viable alternative to traditional CT in many clinical contexts, with the potential for broader application as the technology matures.

Computed tomography (CT) is widely used for the diagnosis and surgical treatment of spinal pathologies, particularly for pedicle screw placement. However, CT’s limitations, notably radiation exposure, necessitate the development of alternative imaging techniques. Synthetic CT (sCT), which generates CT-like images from existing magnetic resonance imaging (MRI) scans, offers a promising alternative to reduce radiation exposure. This study examines the emerging role of sCT in spinal surgery, focusing on usability, efficiency, and potential impact on surgical outcomes. This qualitative literature review evaluated various sCT generation methods, encompassing traditional atlas-based and bulk-density models, as well as advanced convolutional neural network (CNN) architectures, including U-net, V-net, and generative adversarial network models. The review assessed sCT accuracy and clinical feasibility across different medical disciplines, particularly oncology and surgery, with potential applications in orthopedic, neurosurgical, and spinal surgery. sCT has shown significant promise across various medical disciplines. CNN-based techniques enable rapid and accurate generation of sCT from MRI scans, rendering clinical use feasible. sCT has been used to identify pathologies and monitor disease progression, suggesting that MRI alone may suffice for diagnosis and planning in the future. In spinal surgery, sCTs are particularly useful in visualizing key anatomical features like vertebral dimensions and spinal canal diameter. However, challenges persist, especially in visualizing complex structures and larger spinal regions, like the lumbar spine. Additional limitations include inaccuracies stemming from surgical implants and image variability. The application of sCT technology in spinal surgery holds great promise, improving diagnostics, planning, and treatment outcomes. Although further research is required to improve its precision, it offers a viable alternative to traditional CT in many clinical contexts, with the potential for broader application as the technology matures.