Mastaneh Torkamani‐Azar
Papers
2
Total Citations
41
H-Index
2
About
Mastaneh Torkamani‐Azar is a rising researcher at the intersection of surgical data science and human factors engineering, with a focus on enhancing performance and safety in the operating room. Her work uniquely combines deep learning, motion tracking, and cognitive psychology to understand and improve surgical expertise. In her highly cited 2021 paper (36 citations), she pioneered the use of deep learning for automated tool detection in microsurgery, enabling the objective monitoring of kinematics and eye-hand coordination—a critical step toward quantifying surgical skill and providing real-time feedback. More recently, her 2023 mixed-methods systematic review (5 citations) has made a significant contribution by systematically mapping the broad landscape of intraoperative stressors affecting clinical personnel, from surgeons to nurses. This work provides a foundational framework for designing interventions to reduce cognitive load and improve team performance under pressure. By bridging technical innovation with a deep understanding of human cognition, Torkamani‐Azar is helping to shape a future where surgical training and practice are informed by data-driven insights into both the tools and the people who wield them.
Research Focus
Key Achievements
Top Papers
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