Robot-assisted Microsurgery: Lessons Learned from 50 Consecutive Cases
Felix L. Struebing, Amir K. Bigdeli, Jonathan Weigel, Emre Gazyakan, Felix H. Vollbach, Adriana C. Panayi, Julian Vogelpohl, Arne H. Boecker, Ulrich Kneser
- Year
- 2024
- Citations
- 31
Abstract
Background: The potential of robot-assisted surgery in plastic and reconstructive surgery remains to be established, especially in free tissue transfer. This prospective study aimed to present our experience and findings from the first 50 consecutive cases of robot-assisted microsurgery using the Symani surgical system. Methods: A prospective database was maintained, recording patient demographics and surgical details for all cases of robot-assisted microsurgery in a large academic institution. All surgeons underwent an intensive training program with the Symani surgical system. Results: A total of 50 patients who underwent robot-assisted microsurgical reconstruction were identified. Free microsurgical tissue transfer was performed in 45 cases, targeted muscle reinnervation in four cases, and lymphovenous anastomoses in a single case. A total of 94 robot-assisted anastomoses and coaptations were performed, (46 venous and 30 arterial anastomoses, 16 nerve coaptations, two lymphovenous anastomoses). Six cases involved perforator-to-perforator anastomoses. Ninety-eight percent of attempted anastomoses were completed using the robot. Size-mismatch anastomoses, seen in 37.8% of cases, took significantly longer. Minor complications occurred in three cases and major in six cases. There were three cases of microvascular compromise requiring revision. One partial flap loss and no complete flap loss occurred. Conclusions: Our study highlights the immense potential of robot-assisted microsurgery, and a feasible and effective modality for various microsurgical procedures, with outcomes comparable to those of conventional microsurgery. Despite challenges, such as increased operating times and higher costs, the technology offers significant advantages, such as enhanced precision and motion scaling. We identify a slow learning curve and a necessity for higher caseloads.
Keywords
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