Pediatric Robotic-assisted Laparoscopic Pyeloplasty: Defining Mastery Over a 15 Year Experience

Global trends and hotspots in the learning curves of robotic-assisted surgery: a bibliometric and visualization analysis

In recent years, there has been a substantial increase in the number of research papers published in the field of robotic-assisted surgery (RAS). Nevertheless, systematic analyses focusing on the key hotspots associated with the learning curves (LCs) of RAS, global collaboration models, and future trends remain relatively limited. This study employed bibliometric methods to conduct a comprehensive search and analysis of papers on the LC of RAS published in the Web of Science Core Collection between 2005 and 2025. A visual analysis was performed across multiple dimensions, including countries, institutions, sources, and authors. The results revealed an upward trend in the number of publications, with a peak observed in 2024. The United States ranked first in terms of publication volume, while Yonsei University emerged as the most productive institution. Mottrie Alexandre contributed to the highest number of publications, and Dindo d received the highest number of citations. Frequently occurring keywords included "outcome", "experience", "minimally invasive surgery", "revision", and "laparoscopic surgery". Clustering keywords were associated with "rectal cancer", "en-y gastric bypass", "transoral robotic surgery", "spine surgery", and "endometrial cancer". Furthermore, the top five keywords with the strongest citation bursts were "laparoscopic radical prostatectomy", "total mesorectal excision", "da vinci", "prostatectomy", and "mrc clasicc trial". This study offers valuable insights into the future development of this field and supports further exploration and innovation.

Evaluating the trainee impact on robotic surgery learning curves: a CUSUM analysis of large series pediatric robot-assisted laparoscopic pyeloplasty

Robot-assisted laparoscopic pyeloplasty (RALP) is a minimally invasive treatment for ureteropelvic junction obstruction (UPJO). The learning curves for RALP have been well-documented, primarily in post-fellowship surgeons, but the impact of trainee involvement during fellowship training remains unclear. This study aims to (1) establish a large series post-fellowship single surgeon learning curve and (2) analyze the impact of annual trainee involvement on surgical proficiency using cumulative sum (CUSUM) analysis. A retrospective review of pediatric RALP cases from July 2012 to June 2023 at a high-volume institution was conducted. Cases performed by a single post-fellowship surgeon were included. Concomitant procedures and redo surgeries were excluded. Operative time (OT) and complication rates (CR) were tracked using CUSUM analysis. The learning curves were categorized into phases: learning, competency, case-mix, and proficiency. The effect of annual fellowship trainees was analyzed by constructing normalized learning curves per academic year. A total of 220 cases were analyzed. The post-fellowship surgeon reached competency after 29 cases, with proficiency achieved after 99 cases. Trainee involvement demonstrated a characteristic "down-up-down" pattern in OT and was not associated with increased CR during each of 6 years analyzed, reflecting an initial observation phase, increased operative time during hands-on learning, and eventual improvement. In contrast, the single year without a trainee showed a continuous downward OT trend. The complication rates remained below unacceptable thresholds and case complexity influenced OT trends. CUSUM-based analysis effectively maps the RALP learning curve, revealing distinct phases of surgical proficiency and the impact of trainee involvement. The reproducible trainee learning pattern provides insights into optimizing robotic surgery education while maintaining patient safety.