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: Full endoscopic cervical surgery (FECS) is an evolving minimally invasive approach for treating cervical spine disorders. This systematic review synthesizes current evidence on the clinical outcomes and patient perspectives associated with FECS, specifically evaluating its safety, efficacy, and overall patient satisfaction. Methods: A systematic search of the PubMed/MEDLINE, Cochrane Library, Embase, and Web of Science databases was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies published between January 2000 and September 2024 that reported on clinical outcomes or patient perspectives related to FECS were included. Risk of bias was assessed using the ROBINS-I (Risk Of Bias In Non-randomized Studies - of Interventions) tool and the Cochrane Risk of Bias tool. Inclusion criteria encompassed randomized controlled trials, prospective cohort studies, retrospective studies, and observational studies focused on adult populations undergoing FECS for cervical spine surgery. Results: The final synthesis included 30 studies. FECS was associated with significant reductions in both cervical and radicular pain, as well as meaningful functional improvements, measured by standardized clinical scales such as the Neck Disability Index and visual analogue scale. Patient satisfaction rates were consistently high, with most studies reporting satisfaction exceeding 85%. Complication rates were low, primarily involving transient neurological deficits that were typically resolved without the need for further intervention. Nonrandomized studies generally presented a moderate risk of bias due to confounding and selection, whereas randomized controlled trials exhibited a low risk of bias. Conclusion FECS is a safe and effective minimally invasive surgical option for cervical spine disorders associated with substantial pain relief, functional improvement and high levels of patient satisfaction.

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: Full endoscopic cervical surgery (FECS) is an evolving minimally invasive approach for treating cervical spine disorders. This systematic review synthesizes current evidence on the clinical outcomes and patient perspectives associated with FECS, specifically evaluating its safety, efficacy, and overall patient satisfaction. Methods: A systematic search of the PubMed/MEDLINE, Cochrane Library, Embase, and Web of Science databases was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies published between January 2000 and September 2024 that reported on clinical outcomes or patient perspectives related to FECS were included. Risk of bias was assessed using the ROBINS-I (Risk Of Bias In Non-randomized Studies - of Interventions) tool and the Cochrane Risk of Bias tool. Inclusion criteria encompassed randomized controlled trials, prospective cohort studies, retrospective studies, and observational studies focused on adult populations undergoing FECS for cervical spine surgery. Results: The final synthesis included 30 studies. FECS was associated with significant reductions in both cervical and radicular pain, as well as meaningful functional improvements, measured by standardized clinical scales such as the Neck Disability Index and visual analogue scale. Patient satisfaction rates were consistently high, with most studies reporting satisfaction exceeding 85%. Complication rates were low, primarily involving transient neurological deficits that were typically resolved without the need for further intervention. Nonrandomized studies generally presented a moderate risk of bias due to confounding and selection, whereas randomized controlled trials exhibited a low risk of bias. Conclusion FECS is a safe and effective minimally invasive surgical option for cervical spine disorders associated with substantial pain relief, functional improvement and high levels of patient satisfaction.

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.