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Implementation and outcomes in benign gynecological surgery with HUGO™ RAS system 12 months initial experience.
Yael Yagur1,2,3, Martin A Martino4, Mikhail Sarofim5,6
1Sydney Women's Endosurgery Centre (SWEC), St George Hospital, Kogarah, Sydney, NSW, Australia. yaelyagur@gmail.com.
Journal of Robotic Surgery
|September 26, 2024
Summary
This study evaluated the Hugo™ Robotic-Assisted Surgery system in benign gynecological procedures. While adaptable, the system shows promise but is still in its early learning curve phase for surgical teams.
Area of Science:
- Minimally Invasive Surgery
- Gynecological Surgery
- Robotic-Assisted Surgery
Background:
- Robotic-assisted surgery (RAS) offers potential benefits in gynecological procedures.
- The Hugo™ system represents a new platform for minimally invasive gynecological interventions.
Purpose of the Study:
- To evaluate the perioperative outcomes and team experience with the Hugo™ Robotic-Assisted Surgery system in benign gynecological surgeries.
- To assess the system's adaptability, efficiency, and identify initial challenges during its learning curve.
Main Methods:
- Retrospective analysis of 60 patients undergoing elective robotic surgeries for benign gynecological conditions (Feb 2023-Feb 2024).
- Data collected included patient demographics, surgical indications, perioperative metrics (port placement, docking, console time), and procedural outcomes (satisfaction, troubleshooting, complications).
Main Results:
- Procedures included pelvic endometriosis (53%), hysterectomies (27%), and adnexal surgery (10%).
- Mean port-placement time: 13 min 41 s; docking time: 5 min 51 s; console time: 1 h 5 min.
- System demonstrated flexibility in port configurations (31% low-port, 63% asymmetrical arm positioning).
- Identified challenges: arm tremors and limited assistant workspace.
Conclusions:
- The Hugo™ RAS system is adaptable for benign gynecological surgeries, with a potentially rapid learning curve for port placement, docking, and procedure times in well-trained teams.
- Despite initial operational challenges, the system shows promise, indicating it is currently in its learning curve period.

