Endoscopic spine surgery (ESS) is growing in popularity worldwide. An expanding body of literature demonstrates rapid functional recovery with reduced morbidity compared to open techniques. Both full endoscopic spine surgery, or uniportal endoscopy, and unilateral biportal endoscopy (UBE) can be employed in conjunction with various navigation and enabling technologies for assistance with localization of anatomic orientation and assessment of the intraoperative target spinal pathology. This review article describes various navigation technologies in ESS, including 2-dimensional (2D) fluoroscopic imaging, 2D fluoroscopic navigation, 3-dimensional C-arm navigation, augmented reality, and spinal robotics. Employment of enabling navigation and emerging technology with the registration of patient-specific anatomy enables clear delineation of anatomic landmarks and facilitation of a successful procedure. Additionally, avoidance of common pitfalls during use of navigation systems in ESS is discussed in this review.

Study Design: A retrospective study.Purpose: This study aimed to develop machine-learning algorithms for predicting survival in patients who underwent surgery for spinal metastasis.Overview of Literature: This study develops machine-learning models to predict postoperative survival in spinal metastasis patients, filling the gaps of traditional prognostic systems. Utilizing data from 389 patients, the study highlights XGBoost and CatBoost algorithms̓ effectiveness for 90, 180, and 365-day survival predictions, with preoperative serum albumin as a key predictor. These models offer a promising approach for enhancing clinical decision-making and personalized patient care.Methods: A registry of patients who underwent surgery (instrumentation, decompression, or fusion) for spinal metastases between 2004 and 2018 was used. The outcome measure was survival at postoperative days 90, 180, and 365. Preoperative variables were used to develop machine-learning algorithms to predict survival chance in each period. The performance of the algorithms was measured using the area under the receiver operating characteristic curve (AUC).Results: A total of 389 patients were identified, with 90-, 180-, and 365-day mortality rates of 18%, 41%, and 45% postoperatively, respectively. The XGBoost algorithm showed the best performance for predicting 180-day and 365-day survival (AUCs of 0.744 and 0.693, respectively). The CatBoost algorithm demonstrated the best performance for predicting 90-day survival (AUC of 0.758). Serum albumin had the highest positive correlation with survival after surgery.Conclusions: These machine-learning algorithms showed promising results in predicting survival in patients who underwent spinal palliative surgery for spinal metastasis, which may assist surgeons in choosing appropriate treatment and increasing awareness of mortality-related factors before surgery.

Study Design: Retrospective cohort study.Purpose: This study aimed to develop machine-learning algorithms to predict ambulation outcomes following surgery for spinal metastasis.Overview of Literature: Postoperative ambulation status following spinal metastasis surgery is currently difficult to predict. The improved ability to predict this important postoperative outcome would facilitate management decision-making and help in determining realistic treatment goals.Methods: This retrospective study included patients who underwent spinal metastasis at a university-based medical center in Thailand between January 2009 and November 2021. Collected data included preoperative parameters and ambulatory status 90 and 180 days following surgery. Thirteen machine-learning algorithms, namely, artificial neural network, logistic regression, CatBoost classifier, linear discriminant analysis, extreme gradient boosting, extra trees classifier, random forest classifier, gradient boosting classifier, light gradient boosting machine, naïve Bayes, K-neighbor classifier, Ada boost classifier, and decision tree classifier were developed to predict ambulatory status 90 and 180 days following surgery. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and F1-score.Results: In total, 167 patients were enrolled. The number of patients classified as ambulatory 90 and 180 days following surgery was 140 (81.9%) and 137 (82.0%), respectively. The extreme gradient boosting algorithm was found to most accurately predict 180-day ambulatory outcome (AUC, 0.85; F1-score, 0.90), and the decision tree algorithm most accurately predicted 90-day ambulatory outcome (AUC, 0.94; F1-score, 0.88).Conclusions: Machine-learning algorithms were effective in predicting ambulatory status following surgery for spinal metastasis. Based on our data, the extreme gradient boosting and decision tree best predicted postoperative ambulatory status 180 and 90 days after spinal metastasis surgery, respectively.

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This structured debate integrates a narrative evidence synthesis with an international practice-pattern survey comparing uniportal endoscopic cervical foraminotomy (PECF), unilateral biportal endoscopic cervical foraminotomy (UBE-CF), and microscopic/tubular posterior cervical foraminotomy. The literature search covered January 1990 to July 5, 2026. Comparative evidence was predominantly retrospective, single-center, and heterogeneous. Background and expertise data were available for 75 respondents, including 74 with endoscopic expertise, and 73 completed all 4 clinical scenarios. Uniportal-only respondents predominantly selected PECF, whereas biportal-only respondents favored UBE-CF. Treatment choices differed across the predefined clinical scenarios, with anterior cervical discectomy and fusion (ACDF) selected by 19 of 73 (26.0%), 35 of 73 (47.9%), 19 of 73 (26.0%), and 49 of 73 respondents (67.1%) in cases 1–4, respectively. These scenarios changed several clinical features simultaneously and do not establish the independent influence of any single feature. Available comparative studies have not established durable superiority of one posterior technique over another. ACDF was the most frequently selected single procedure in the scenario combining retrolisthesis, asymptomatic central stenosis, and motor weakness (case 2) and in the revision scenario (case 4). Platform selection should reflect surgeon expertise, anatomy, and available resources. Multicenter randomized or expertise-based comparative trials with standardized outcomes and long-term follow-up are needed.