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Technical Strategies and Learning Curve in Robotic-assisted Peripheral Nerve Surgery
Martin Aman1,2, Felix Struebing1,2, Jonathan Weigel1,2
1From the Department of Hand, Plastic and Reconstructive Surgery, Burn Center, B.G. Trauma Center Ludwigshafen, Ludwigshafen, Germany.
Plastic and Reconstructive Surgery. Global Open
|October 10, 2024
Summary
Robotic-assisted peripheral nerve surgery (RASPN) enhances microsurgical precision. This study of 19 patients found no significant learning curve effect on stitch time, highlighting the need for further optimization.
Area of Science:
- Microsurgery
- Neurosurgery
- Surgical Robotics
Background:
- Robotic-assisted peripheral nerve surgery (RASPN) offers enhanced precision and tremor reduction for nerve coaptations.
- This study presents the largest patient collective investigating RASPN, detailing technical aspects, operative setups, and learning curves.
Purpose of the Study:
- To investigate the largest published patient collective in robotic-assisted peripheral nerve surgery (RASPN).
- To analyze technical aspects, operative setups, and the learning curve associated with RASPN.
Main Methods:
- A prospective database collected surgical details including surgery type, duration, nerve coaptation time, and stitch count.
- An experienced surgeon completed a 12-hour training program on the Symani robot system with optical magnification before clinical use.
Main Results:
- Nineteen patients underwent robot-assisted peripheral nerve reconstruction, including nerve transfers, targeted muscle reinnervation, neurotized free flaps, and autologous nerve grafts.
- Learning curve analysis showed no significant difference in time per stitch between the initial nine coaptations (4.9 ± 0.5 min) and the last ten coaptations (5.5 ± 1.5 min).
Conclusions:
- The learning curve for RASPN was compared to early experiences with other surgical robots, emphasizing surgeon and assistant training.
- Obstacles like instrument grip strength and blood clots were noted; ongoing research is needed to optimize RASPN for enhanced precision.
