ArticleSurgical endoscopy2026
Single-surgeon learning curve for robotic-assisted transabdominal retromuscular repair with Hugo RAS™ surgical system.
Article in Surgical endoscopy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Machine learning for operative time prediction in robotic ventral hernia repair patients: Perspective on predictive model development and validation.Langenbeck's archives of surgery · 2026Article
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8 authors.
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Abstract
backgroundIn September 2024, we started using the Hugo Ras™ for minimally invasive repair of ventral hernia. We aim to analyze the learning curve during the initial experience of a single surgeon with this new robotic platform.
methodsWe prospectively analyzed consecutive patients (September/2024-April/2025) undergoing robotic-assisted transabdominal retromuscular repair (r-TARM) using the Hugo RAS™ platform. All the surgical procedures were performed by a single surgeon without previous experience in human robotic ventral hernia repair. The operative times during the separate steps of the surgical procedure were Skin-to-skin, Non-surgical, Console, Dissection, Defect closure, Mesh placement, and Posterior rectus sheet closure. Complications were reported up to 8 weeks postoperatively. Operative times were divided into tertiles and analyzed. Differences across the tertiles were evaluated according to analysis of variance. Linear regression models and cumulative sum charts (CUSUM) were used to describe the change in operative times and assess improvement.
resultsForty-two patients with either a primary (n = 26) or an incisional ventral hernia (n = 16) were identified. All hernias had a mean diameter < 6 cm. Three conversions to laparoscopic surgery occurred during the peritoneal closure. Console time (1st tertile 177.5 ± 44.3 min versus 3rd tertile 138.5 ± 20.1 min, p = 0.005) and Dissection time (1st tertile 69.8 ± 22.2 min versus 3rd tertile 45.7 ± 12.1 min, p = 0.003) decreased significantly. Console and Dissection times showed significant changes in performance after 34 and 37 operations, respectively.
conclusionsThe operative time decrease while learning r-TARM with the Hugo RAS™ platform was related to reduced Dissection time. After 37 cases, there was a drop in the Dissection time.
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