Journal of Robotic Surgery最新文献

Transoral laser microsurgery and transoral robot-assisted surgery are central to organ-preserving management of oropharyngeal squamous cell carcinoma, yet their comparative oncologic and functional performance remains uncertain. We conducted a systematic review and meta-analysis of studies reporting outcomes of adults with histologically confirmed oropharyngeal squamous cell carcinoma treated with either technique as primary transoral therapy. Primary outcomes were overall survival, disease-specific survival, and locoregional recurrence. Proportions and continuous variables were pooled using random-effects models. Seventy-two studies including 20,536 patients were analyzed, comprising 19,036 treated with transoral robotic surgery and 1,500 with transoral laser microsurgery. Baseline characteristics were comparable, with predominantly T1-T2 tonsil or base-of-tongue tumors and high human papillomavirus positivity. Pooled overall survival was higher for transoral robotic surgery than for transoral laser microsurgery (0.93 vs. 0.86; p = 0.0002), while disease-specific survival was identical between techniques (0.94 vs. 0.94; p = 0.61). Rates of local, regional, and overall recurrence showed no significant differences (p > 0.05 for all). Positive margins occurred in 13% and 8% of cases, respectively (p = 0.16). Functional outcomes were generally favorable in both groups, with similar rates of gastrostomy, tracheostomy, poor swallowing, and poor voice. Postoperative bleeding (3% vs. 6%) and overall complications (7% vs. 6%) were low across studies (p > 0.05 for all). Both transoral laser microsurgery and transoral robotic surgery provide excellent disease-specific survival, low recurrence rates, and comparable functional and safety profiles in oropharyngeal squamous cell carcinoma. The higher pooled overall survival observed with transoral robotic surgery likely reflects case-mix rather than intrinsic oncologic superiority, supporting both techniques as guideline-concordant options within organ-preserving treatment pathways.

Robotic-assisted total knee arthroplasty (RA-TKA) aims to improve implant positioning and soft tissue balance, but few studies have evaluated the learning curve (LC) for intraoperative ligament balancing.

Methods: We retrospectively analyzed 234 posterior-stabilized RA-TKAs performed by three surgeons with varying prior experience using the MAKO system. The primary endpoint was the learning curve duration for mastering ligament balancing, classified as perfectly balanced, stable, or unstable based on medial and lateral joint line openings in extension and flexion. Learning curves for ligament balancing and operative time were assessed using LC-CUSUM analysis.

Results: Overall, 79.8% of TKAs were perfectly balanced, 19.1% stable, and 1.1% unstable. The LC for overall ligament balancing ranged from 5 to 9 cases depending on the surgeon. Operative time normalized after 12 to 22 cases, shorter for surgeons with prior navigation experience. Complications were minimal, with no early infections and few mechanical issues.

Conclusions: Optimal ligament balancing with RA-TKA is achieved rapidly and remains stable, with operative times comparable to conventional techniques after the LC. Prior experience with navigation-assisted surgery shortens the LC. These findings support the reproducibility and safety of robotic ligament balancing, though further studies are needed to confirm long-term clinical benefits.

AI-enhanced robotic hands are rapidly reshaping tumour surgery by merging real-time sensing, precision mechanics, and intelligent decision support, yet current systems still struggle with early lesion detection, limited tactile sensitivity, and inconsistent accuracy across cancer types. This review addresses these gaps by examining how next-generation robotic hands, empowered by multimodal AI, augmented imaging, hybrid guidance, and minimally invasive mechatronics, can improve early tumour localization and safer resections. The study synthesizes insights from urologic, breast, colorectal, gastric, thoracic, and gynecologic oncology to highlight shared trends such as the shift toward personalized robotics, smart biopsy tools, light-mediated theranostics, flexible platforms, and real-time intraoperative analytics. A comparative reading of quantitative and qualitative evidence reveals strong gains in surgical precision and patient outcomes, yet also contradictions regarding cost-effectiveness, reproducibility of AI predictions, and disparity in adoption between high- and low-resource settings. Using a narrative review approach, key findings point to robotic hands with enhanced tactile sensors and AI-driven micro-maneuvering as promising breakthroughs for detecting microtumours, reducing positive margins, and guiding on-table diagnostics. Recommendations emphasize stronger clinical validation, interoperable imaging ecosystems, and ethical design. The implications extend to safer surgeries, shorter recovery, and more equitable cancer care. Limitations include heterogeneous study designs and early-stage prototypes. Future research should explore adaptive learning models, haptic-guided autonomy, and broader trials. Overall, AI-enhanced robotic hands signal a transformative pathway for earlier detection and more precise tumour removal.