Laparoscopic Roux-en-Y Gastric Bypass with the Maestro™ System: a Novel Collaborative Robotic Platform

Abstract Background Laparoscopic Roux-en-Y gastric bypass (LRYGB) is a cornerstone of bariatric surgery, yet its complexity poses challenges in visualization and retraction, often compounded by variability in surgical assistance. The Maestro™ is an Artificial Intelligence (AI)-powered surgical platform designed to elevate minimally invasive surgical performance and technique while keeping the surgeon at the patient’s bedside during the procedure. Objective This study aims to assess the effects of this innovative platform by comparing it with traditional L-RYGB. Methods This retrospective, matched cohort study included patients who underwent Maestro™ (MA-RYGB) and traditional laparoscopic Roux-en-Y gastric bypass (L-RYGB) at a single institution. The 54 Maestro™-assisted RYGB procedures were included prospectively. Each case was retrospectively matched in a 1:1 ratio with a baseline laparoscopic RYGB performed by the same surgical team. Operative time, in-room time, and related variables were recorded for both groups. Statistical Process Control (SPC) charts were generated to evaluate the process stability and variability for operative times. Results The Maestro™ demonstrated a 19.3% reduction in operative time: MA-RYGB 62.9 min vs.75.1 min ( p < 0.001). Operative times appeared more stable across consecutive cases in the Maestro cohort compared with conventional laparoscopy. Postoperative outcomes were comparable. No device-related complications or conversions occurred. Conclusion Maestro™ reduces procedure duration and was associated with a more stable operative course, without compromising safety. The use of the Maestro™ and may help standardize outcomes and operating room performance. This initial work suggests important implications for operative quality and efficiency with this promising new technology.