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About

Kenzo Mestdagh is a pioneering researcher at the intersection of robotic surgery and artificial intelligence, with a primary focus on advancing augmented reality (AR) and computer vision for urological procedures. His most impactful work, "Improving Augmented Reality Through Deep Learning: Real-time Instrument Delineation in Robotic Renal Surgery" (69 citations), directly addresses a critical barrier in surgical AR—the poor visibility of instruments during superimposition of 3D models. By developing deep learning methods for real-time instrument delineation, Mestdagh has significantly enhanced the practical integration of AR in robotic renal surgery. He further contributes to the foundational infrastructure of surgical AI through his multicentric exploration of tool annotation (31 citations), providing essential lessons for teams initiating AI projects in the operating room. Demonstrating a commitment to ethical data sharing, Mestdagh also pioneered a privacy-proof live surgery streaming algorithm (6 citations) that reliably anonymizes out-of-body images across multiple robotic platforms. His recent comparative analysis of robotic ileal ureter replacement versus kidney autotransplantation (2025) showcases his ongoing dedication to optimizing surgical outcomes for complex ureteric strictures. Through these contributions, Mestdagh is shaping a future where robotic surgery is more intelligent, transparent, and widely accessible.