Mojtaba Yeganejou

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About

Mojtaba Yeganejou is a researcher advancing the field of robot-assisted surgery through data-driven performance assessment. His primary research areas include surgical skill evaluation, medical robotics, and machine learning for healthcare applications. Yeganejou’s most cited work, “Surgical Skill Evaluation From Robot-Assisted Surgery Recordings” (2021, 17 citations), addresses a critical challenge in surgical training: the need for objective, quantitative methods to assess trainee proficiency. By developing techniques to analyze recordings from robot-assisted procedures, he offers a path away from traditional, time-consuming, and subjective qualitative evaluations. This contribution directly impacts patient safety and surgical education, providing a scalable framework for automated skill assessment. His work is notable for bridging the gap between robotic surgery data and actionable feedback, enabling trainees to achieve necessary expertise before operating on patients. Yeganejou’s research continues to shape how surgical competence is measured, with implications for reducing bias and improving outcomes in minimally invasive surgery.