Pediatric Robotic Surgery: From Observation to Autonomy in a Motion-Based Assessment of Learning Curve Using a Dual-Console System

Abstract

Introduction:

Objective data quantifying the trend of the robotic-assisted learning process remains limited. The aim of this study is to evaluate the efficiency and progression of surgical skill acquisition during robotic-assisted pyeloplasty by comparing performance parameters between expert and non-expert surgeons using a dual-console system over a 3-year period.

Methods:

A total of 25 robotic-assisted pyeloplasties were retrospectively analyzed; all were performed by a Da Vinci Xi robotic platform equipped with dual operative consoles, operated simultaneously by a robotic expert (C1) and a non-expert surgeon (C2). The procedures were divided into five standardized phases. Surgical performance was assessed using Tracker^® motion analysis software, which measured operative times, console utilization, instrument collisions, and motion efficiency. Trends across years were assessed using one-way analysis of variance (P < .05).

Results:

A total of 25 cases were analyzed. The total operative time decreased significantly from 210 minutes in year 1 to 185 minutes in year 3 (P = .008). The expert surgeon’s console time ratio decreased from 90.0% in year 1 to 14.5% in year 3 (P < .001), with a corresponding increase in non-expert surgeon’s console time. A significant reduction in the number of instrument collisions (from 1457 in year 1 to 97 in year 3, P < .001) and a marked increase in motion efficiency (from 14% to 88%, P < .001) were observed for the non-expert surgeon. Phase-specific analysis revealed a steady improvement in the non-expert surgeon’s performance across all phases.

Conclusions:

Robotic-assisted pyeloplasty significantly accelerates skill acquisition for non-expert surgeons, as evidenced by improvements in operative time, instrument collisions, and motion efficiency. The dual-console approach provides a valuable educational framework, facilitating real-time supervision and progressive task delegation. These findings support integrating dual-console systems and objective motion analysis into structured robotic training programs to enhance surgical proficiency and safety in pediatric surgery.