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About

F. Cisternino is a leading researcher at the intersection of computer vision, deep learning, and robotic surgery, with a primary focus on advancing augmented reality (AR) and automated surgical analysis. Their most impactful work, "Improving Augmented Reality Through Deep Learning: Real-time Instrument Delineation in Robotic Renal Surgery" (2023, 69 citations), tackles a critical barrier to AR adoption in the operating room: the poor visibility of surgical instruments during overlay. By developing a deep learning method for real-time instrument delineation, Cisternino enables more accurate and clinically viable AR guidance, directly enhancing surgeon precision in complex procedures like renal surgery. This contribution addresses core challenges of model alignment and deformation, making AR integration more practical. Additionally, Cisternino co-authored "SAR-RARP50: Segmentation of surgical instrumentation and Action Recognition on Robot-Assisted Radical Prostatectomy Challenge" (2023, 11 citations), a benchmark study that advances foundational computer-assisted intervention tasks—surgical tool segmentation and action recognition—which are essential for skills assessment and decision support systems. Through these works, Cisternino demonstrates a clear commitment to translating deep learning innovations into tangible improvements for robotic surgery, bridging the gap between algorithmic research and real-world clinical impact.