Gabriel Gaggiotti

About

Gabriel Gaggiotti is a leading researcher in orthopedic surgery, specializing in the optimization of unicompartmental knee arthroplasty (UKA) through advanced robotic assistance. His work focuses on enhancing surgical precision and implant positioning for patients with medial knee osteoarthritis. Gaggiotti’s major contributions include demonstrating that image-based robotic systems achieve significantly higher accuracy in implant placement than imageless systems, a finding supported by his 2024 study of 292 knees (12 citations). He also pioneered a validated method using preoperative valgus stress radiographs to accurately anticipate bone resections in UKA, streamlining surgical planning (5 citations). His 2025 investigation into simultaneous bilateral UKA (126 knees) confirmed that robotic assistance enhances radiographic accuracy while maintaining safety comparable to conventional methods (2 citations). By systematically comparing robotic platforms and validating planning protocols, Gaggiotti has advanced the evidence base for precision joint replacement, directly impacting surgical decision-making and patient outcomes in minimally invasive knee surgery.

Research Focus

Key Achievements

Top Papers

1. 1
   Enhancing robotic precision in medial UKA: Image‐based robot‐assisted system had higher accuracy in implant positioning than imageless robot‐assisted system across 292 knees

   12 citations · 2024
2. 2
   Valgus stress knee radiographs accurately anticipate the bone resection in medial unicompartmental knee arthroplasty: Protocol validation using an image‐based robotic system

   5 citations · 2024
3. 3
   Robotic Assistance in Simultaneous Bilateral Medial Unicompartmental Knee Arthroplasty: A Retrospective Cohort Study of 126 Knees Demonstrating Enhanced Radiographic Accuracy and Comparable Safety to Conventional Methods

   2 citations · 2025

Key Collaborators

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