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About

Amir Baghdadi’s research sits at the intersection of computer vision, surgical simulation, and data analytics, with a focus on transforming how surgical proficiency is assessed and taught. His most cited work introduces a computer vision technique that automatically evaluates surgical performance by analyzing surgeons’ console-feed videos—a breakthrough that earned 59 citations and promises to reduce the steep learning curve in complex procedures. Baghdadi also contributed to the development of the Marion Surgical K181, a virtual reality simulator for Percutaneous Nephrolithotomy (PCNL), a high-stakes kidney stone removal procedure that typically requires 36 to 60 cases for clinical proficiency. His evaluation of this simulator (33 citations) provides critical evidence for its use in training. Further expanding his impact, Baghdadi employed sensor-based kinematics and recorded surgeon experience to assess a microsurgery-specific haptic device, demonstrating how data analytics can quantify dexterity in robot-assisted surgery (13 citations). His proof-of-concept work on automated performance modeling, presented at the Journal of Urology, underscores his role in advancing objective, data-driven surgical training. Through these contributions, Baghdadi is shaping the future of surgical education and skill assessment.