Mohammad Karamzadeh
Papers
1
Total Citations
4
H-Index
1
About
Mohammad Karamzadeh is a researcher at the intersection of surgical robotics and deep learning, with a primary focus on developing intelligent systems for minimally invasive procedures. His work centers on force estimation in robotic-assisted surgery, a critical challenge for enhancing haptic feedback and surgical precision. In his most-cited paper, "Supervised Deep Learning with Finite Element Generated Data for Force Estimation in Robotic-Assisted Liver Surgery" (2022), Karamzadeh pioneered a novel approach that leverages finite element modeling to generate synthetic training data for supervised deep learning models. This method addresses the scarcity of real-world force data in delicate soft-tissue surgeries, enabling accurate, non-invasive force estimation without additional sensors. His contributions have significant implications for improving patient safety and surgical outcomes in liver procedures. With 4 citations, this work has already garnered attention from the surgical robotics community, laying a foundation for future advancements in data-driven haptic feedback systems. Karamzadeh’s research exemplifies the synergy between computational modeling and clinical application, offering a scalable solution for force sensing in complex surgical environments.
Research Focus
Key Achievements
Top Papers
- 1