Supermicrosurgery involving vessels ≤0.3 mm remains among the most technically demanding procedures in reconstructive surgery. Robotic platforms have shown potential to enhance precision, but their feasibility at this extreme scale requires further validation.

Apprentice surgeons (≤5 years' experience) performed rat femoral vessel (approximately 0.26–0.30 mm) anastomoses using both hand-sewn and robotic-assisted techniques across eight consecutive trials. Outcome measures included Structured Assessment of Robotic Microsurgical Skills (SARMS) scores, operative time, stitch count, and patency rate. Learning curves were assessed with cumulative summation (CUSUM) analysis, and anastomotic quality was evaluated using scanning electron microscopy (SEM).

Robotic-assisted anastomosis reached proficiency earlier than hand-sewn repair (4th versus 6th trial). Although initial operative time was longer for robotic cases (1,772 ± 120 seconds versus 1,355 ± 187 seconds, p = 0.013), times equalized by the 5th trial and decreased by 42% by the 8th (1,026 ± 58 seconds versus 1,023 ± 73 seconds, p = 0.959). Stitch counts were higher in later robotic trials (4.2 ± 0.6 versus 3.5 ± 0.5, p = 0.015). Both groups achieved 100% patency by the 4th trial. CUSUM and SEM analyses confirmed a steeper learning curve and superior anastomotic precision with robotic assistance.

Robotic-assisted supermicrosurgery enables earlier proficiency, enhanced precision, and reproducible anastomotic quality compared with hand-sewn techniques. Despite longer initial times, robotic performance rapidly improved, achieving equivalent efficiency with greater technical control. These findings confirm the technical feasibility of robotic supermicrosurgery at the 0.3 mm scale and support its potential role in microsurgical and lymphatic reconstruction training and clinical practice.

Received: 08 October 2025

Accepted after revision: 25 February 2026

Article published online:
09 April 2026

© 2026. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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