Dataset and Analysis of Long-Term Skill Acquisition in Robot-Assisted Minimally Invasive Surgery

2025-03-27

AI Summary

This study investigates longitudinal skill acquisition in robotic-assisted minimally invasive surgery among surgical residents, focusing on the effects of training interval spacing and 26-hour on-call fatigue on performance in three core tasks: Ring Tower Transfer, Knot-Tying, and Suturing. An eighteen-resident, six-month longitudinal dry-lab training program was conducted, yielding the first multimodal, multi-temporal dataset—comprising 972 trials—with synchronized video, kinematic, behavioral tracking, and high-resolution suture-pad scanning data collected pre-, during, and post-call. Results reveal asymmetric cross-month learning and forgetting dynamics, as well as task-specific modulation of performance metrics by fatigue. The study provides critical empirical evidence and an open-source dataset to inform AI-driven adaptive surgical training systems.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Objective: We aim to investigate long-term robotic surgical skill acquisition among surgical residents and the effects of training intervals and fatigue on performance. Methods: For six months, surgical residents participated in three training sessions once a month, surrounding a single 26-hour hospital shift. In each shift, they participated in training sessions scheduled before, during, and after the shift. In each training session, they performed three dry-lab training tasks: Ring Tower Transfer, Knot-Tying, and Suturing. We collected a comprehensive dataset, including videos synchronized with kinematic data, activity tracking, and scans of the suturing pads. Results: We collected a dataset of 972 trials performed by 18 residents of different surgical specializations. Participants demonstrated consistent performance improvement across all tasks. In addition, we found variations in between-shift learning and forgetting across metrics and tasks, and hints for possible effects of fatigue. Conclusion: The findings from our first analysis shed light on the long-term learning processes of robotic surgical skills with extended intervals and varying levels of fatigue. Significance: This study lays the groundwork for future research aimed at optimizing training protocols and enhancing AI applications in surgery, ultimately contributing to improved patient outcomes. The dataset will be made available upon acceptance of our journal submission.

Problem

• Investigates long-term robotic surgical skill acquisition in residents
• Examines effects of training intervals and fatigue on performance
• Aims to optimize training protocols for improved patient outcomes

Innovation

• Long-term robotic skill tracking with kinematic data
• Training sessions around 26-hour hospital shifts
• Comprehensive dataset including videos and activity tracking