Objective: This review aimed to synthesize and critically appraise the existing evidence on endoscopic-assisted anterior odontoid screw fixation, with the objectives of clarifying its current clinical utility and identifying priorities for future optimization of the technique. Methods: A systematic review was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines and registered with PROSPERO (registration number: CRD420251251051). Searches of PubMed/MEDLINE, Embase, Scopus, Web of Science, and Google Scholar identified studies reporting the use of endoscopic visualization during anterior odontoid screw placement. Eligible study designs included clinical reports and cadaveric investigations. Extracted data encompassed patient demographics, fracture subtype, operative technique, radiological alignment, fusion status, perioperative complications, and risk of bias, which was assessed using the modified Joanna Briggs Institute checklist. Results: Eight studies met the inclusion criteria, comprising 6 clinical reports and 2 cadaveric feasibility studies. The extent of endoscopic assistance ranged from hybrid mini-open approaches to fully endoscopic uniportal techniques. Reported fusion rates ranged from 80% to 100%, with fully endoscopic procedures demonstrating the most consistent radiological consolidation and anatomical reduction. No major neurovascular, aerodigestive, or implant-related complications were reported. Cadaveric evidence confirmed enhanced visualization of the C2 odontoid entry point and validated safe screw entry-point orientation with acceptable screw trajectories. Risk-of-bias assessment indicated low risk in 5 studies and moderate risk in 3. Small sample sizes, heterogeneous fracture morphology, and modest follow-up durations limited the overall quality of the evidence. Conclusion Endoscopic-assisted anterior odontoid screw fixation appears to be a technically feasible and biologically favorable minimally invasive option for selected odontoid fractures. Its potential advantages include improved visualization and reduced approach-related morbidity. Further prospective, multicenter investigations, particularly those integrating navigation systems, are required to define its definitive role in the management of cervical spine trauma.

Objective: Cervical spine surgery presents unique technical challenges because of the small and complex anatomy of the cervical vertebrae and the high risk of neurovascular complications. Recent advances in 3-dimensional (3D) printing and navigation technologies have been introduced to improve screw placement accuracy, reduce surgical risk, and potentially shorten operative time. In this systematic review, we aimed to summarize currently available evidence and to describe selected studies evaluating 3D-printed navigation templates and intraoperative computer-assisted navigation systems for cervical pedicle screw placement. Methods: This study was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A literature search was performed for publications published between 2019 and 2024 using the following search terms: (“cervical spine” OR “navigation-assisted system”) OR (“3D printing template” OR “computer-assisted system”). A total of 130 articles met the initial screening criteria, of which 8 studies were included in the final analysis after application of predefined inclusion and exclusion criteria. Results: Eight studies were included in the final systematic review and network meta-analysis. The findings indicated that 3D-printed templates demonstrated higher accuracy, reaching up to 95.8% (risk ratio [RR], 1.17), and fewer deviations compared with computer-assisted navigation (RR, 1.05) and traditional techniques (RR, 1.0). Computer-assisted navigation showed greater heterogeneity across studies. However, no statistically significant difference in outcomes was observed between 3D-printed templates and computer-assisted navigation systems. Conclusion Navigation-assisted cervical pedicle screw fixation is effective, and both 3D-printed templates and computer-assisted navigation techniques provide advantages in terms of accuracy and procedural safety.

Obesity presents significant challenges in spinal surgery, including higher rates of perioperative complications, prolonged operative times, and delayed recovery. Traditional open spine surgery often exacerbates these risks, particularly in patients with obesity, because of extensive tissue dissection and larger incisions. Endoscopic spine surgery (ESS) has emerged as a promising minimally invasive alternative, offering advantages such as reduced tissue trauma, minimal blood loss, lower infection rates, and faster recovery. This systematic review and meta-analysis aimed to evaluate the safety, efficacy, and outcomes of ESS techniques, including fully endoscopic and biportal endoscopic lumbar discectomy and decompression, in patients with obesity and lumbar spine pathologies. A comprehensive literature search of the PubMed/Medline, Embase, and Scopus databases yielded 2,975 studies published between 2000 and 2024, of which 10 met the inclusion criteria. The meta-analysis revealed significant improvements in pain relief (Visual Analog Scale) and functional outcomes (Oswestry Disability Index), with comparable results between patients with and without obesity. Patients who are obese experienced longer operative times and have a slightly higher risk of symptom recurrence; however, ESS demonstrated lower rates of wound infections, shorter hospital stays, and faster recovery than traditional surgery. These findings position ESS as a viable and effective option for managing lumbar spine conditions in patients with obesity, addressing obesity-related surgical challenges while maintaining favorable clinical outcomes. However, limitations such as study heterogeneity and the lack of randomized controlled trials highlight the need for further high-quality research to refine ESS techniques and optimize patient care in this high-risk population.

Ossification of the posterior longitudinal ligament (OPLL) is a significant spinal condition that can lead to severe neurological deficits. Recent advancements in machine learning (ML) and deep learning (DL) have led to the development of promising tools for the early detection and diagnosis of OPLL. This systematic review evaluated the diagnostic performance of ML and DL models and clinical implications in OPLL detection. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed/Medline and Scopus databases were searched for studies published between January 2000 and September 2024. Eligible studies included those utilizing ML or DL models for OPLL detection using imaging data. All studies were assessed for the risk of bias using appropriate tools. The key performance metrics, including accuracy, sensitivity, specificity, and area under the curve (AUC), were analyzed. Eleven studies, comprising a total of 6,031 patients, were included. The ML and DL models demonstrated high diagnostic performance, with accuracy rates ranging from 69.6% to 98.9% and AUC values up to 0.99. Convolutional neural networks and random forest models were the most used approaches. The overall risk of bias was moderate, and concerns were primarily related to participant selection and missing data. In conclusion, ML and DL models show great potential for accurate detection of OPLL, particularly when integrated with imaging techniques. However, to ensure clinical applicability, further research is warranted to validate these findings in more extensive and diverse populations.

Obesity presents significant challenges in spinal surgery, including higher rates of perioperative complications, prolonged operative times, and delayed recovery. Traditional open spine surgery often exacerbates these risks, particularly in patients with obesity, because of extensive tissue dissection and larger incisions. Endoscopic spine surgery (ESS) has emerged as a promising minimally invasive alternative, offering advantages such as reduced tissue trauma, minimal blood loss, lower infection rates, and faster recovery. This systematic review and meta-analysis aimed to evaluate the safety, efficacy, and outcomes of ESS techniques, including fully endoscopic and biportal endoscopic lumbar discectomy and decompression, in patients with obesity and lumbar spine pathologies. A comprehensive literature search of the PubMed/Medline, Embase, and Scopus databases yielded 2,975 studies published between 2000 and 2024, of which 10 met the inclusion criteria. The meta-analysis revealed significant improvements in pain relief (Visual Analog Scale) and functional outcomes (Oswestry Disability Index), with comparable results between patients with and without obesity. Patients who are obese experienced longer operative times and have a slightly higher risk of symptom recurrence; however, ESS demonstrated lower rates of wound infections, shorter hospital stays, and faster recovery than traditional surgery. These findings position ESS as a viable and effective option for managing lumbar spine conditions in patients with obesity, addressing obesity-related surgical challenges while maintaining favorable clinical outcomes. However, limitations such as study heterogeneity and the lack of randomized controlled trials highlight the need for further high-quality research to refine ESS techniques and optimize patient care in this high-risk population.

Ossification of the posterior longitudinal ligament (OPLL) is a significant spinal condition that can lead to severe neurological deficits. Recent advancements in machine learning (ML) and deep learning (DL) have led to the development of promising tools for the early detection and diagnosis of OPLL. This systematic review evaluated the diagnostic performance of ML and DL models and clinical implications in OPLL detection. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed/Medline and Scopus databases were searched for studies published between January 2000 and September 2024. Eligible studies included those utilizing ML or DL models for OPLL detection using imaging data. All studies were assessed for the risk of bias using appropriate tools. The key performance metrics, including accuracy, sensitivity, specificity, and area under the curve (AUC), were analyzed. Eleven studies, comprising a total of 6,031 patients, were included. The ML and DL models demonstrated high diagnostic performance, with accuracy rates ranging from 69.6% to 98.9% and AUC values up to 0.99. Convolutional neural networks and random forest models were the most used approaches. The overall risk of bias was moderate, and concerns were primarily related to participant selection and missing data. In conclusion, ML and DL models show great potential for accurate detection of OPLL, particularly when integrated with imaging techniques. However, to ensure clinical applicability, further research is warranted to validate these findings in more extensive and diverse populations.

Obesity presents significant challenges in spinal surgery, including higher rates of perioperative complications, prolonged operative times, and delayed recovery. Traditional open spine surgery often exacerbates these risks, particularly in patients with obesity, because of extensive tissue dissection and larger incisions. Endoscopic spine surgery (ESS) has emerged as a promising minimally invasive alternative, offering advantages such as reduced tissue trauma, minimal blood loss, lower infection rates, and faster recovery. This systematic review and meta-analysis aimed to evaluate the safety, efficacy, and outcomes of ESS techniques, including fully endoscopic and biportal endoscopic lumbar discectomy and decompression, in patients with obesity and lumbar spine pathologies. A comprehensive literature search of the PubMed/Medline, Embase, and Scopus databases yielded 2,975 studies published between 2000 and 2024, of which 10 met the inclusion criteria. The meta-analysis revealed significant improvements in pain relief (Visual Analog Scale) and functional outcomes (Oswestry Disability Index), with comparable results between patients with and without obesity. Patients who are obese experienced longer operative times and have a slightly higher risk of symptom recurrence; however, ESS demonstrated lower rates of wound infections, shorter hospital stays, and faster recovery than traditional surgery. These findings position ESS as a viable and effective option for managing lumbar spine conditions in patients with obesity, addressing obesity-related surgical challenges while maintaining favorable clinical outcomes. However, limitations such as study heterogeneity and the lack of randomized controlled trials highlight the need for further high-quality research to refine ESS techniques and optimize patient care in this high-risk population.

Ossification of the posterior longitudinal ligament (OPLL) is a significant spinal condition that can lead to severe neurological deficits. Recent advancements in machine learning (ML) and deep learning (DL) have led to the development of promising tools for the early detection and diagnosis of OPLL. This systematic review evaluated the diagnostic performance of ML and DL models and clinical implications in OPLL detection. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed/Medline and Scopus databases were searched for studies published between January 2000 and September 2024. Eligible studies included those utilizing ML or DL models for OPLL detection using imaging data. All studies were assessed for the risk of bias using appropriate tools. The key performance metrics, including accuracy, sensitivity, specificity, and area under the curve (AUC), were analyzed. Eleven studies, comprising a total of 6,031 patients, were included. The ML and DL models demonstrated high diagnostic performance, with accuracy rates ranging from 69.6% to 98.9% and AUC values up to 0.99. Convolutional neural networks and random forest models were the most used approaches. The overall risk of bias was moderate, and concerns were primarily related to participant selection and missing data. In conclusion, ML and DL models show great potential for accurate detection of OPLL, particularly when integrated with imaging techniques. However, to ensure clinical applicability, further research is warranted to validate these findings in more extensive and diverse populations.

Objective: To develop and evaluate a technique using convolutional neural networks (CNNs) for the computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. By leveraging deep learning techniques, the study might potentially lead to improved patient outcomes and clinical decision-making. Methods: This study obtained 500 lateral radiographic cervical spine x-ray images from standard open-source dataset repositories to develop a classification model using CNNs. All the images contained diagnostic information, including normal cervical radiographic images (n=250) and fracture images of the cervical spine fracture (n=250). The model would classify whether the patient had a cervical spine fracture or not. Seventy percent of the images were training data sets used for model training, and 30% were for testing. Konstanz Information Miner (KNIME)’s graphic user interface-based programming enabled class label annotation, data preprocessing, CNNs model training, and performance evaluation. Results: The performance evaluation of a model for detecting cervical spine fractures presents compelling results across various metrics. This model exhibits high sensitivity (recall) values of 0.886 for fractures and 0.957 for normal cases, indicating its proficiency in identifying true positives. Precision values of 0.954 for fractures and 0.893 for normal cases highlight the model’s ability to minimize false positives. With specificity values of 0.957 for fractures and 0.886 for normal cases, the model effectively identifies true negatives. The overall accuracy of 92.14% highlights its reliability in correctly classifying cases by the area under the receiver operating characteristic curve. Conclusion We successfully used deep learning models for computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. This approach can assist the radiologist in screening, detecting, and diagnosing cervical spine fractures.