Megumi Nakao
Papers
4
Total Citations
28
H-Index
4
About
Megumi Nakao’s research lies at the intersection of haptics, surgical robotics, and medical image simulation, with a focus on enhancing safety and precision in minimally invasive procedures. A key contribution is the concept of “pseudo-haptic feedback” in robot-assisted surgery: Nakao’s team demonstrated that surgeons can perceive haptic-like sensations even when haptics are absent, and they developed a vision-based deep learning method to estimate manipulation forces from laparoscopic images—work that has already garnered 9 citations. Earlier foundational studies, such as the 2004 paper on “Practical Haptic Navigation with Clickable 3D Region Input Interface,” proposed haptic navigation systems to help surgeons avoid collisions during master-slave robotic surgery, earning 9 and 6 citations respectively. Nakao also pioneered the Resection Process Map (RPM), a dynamic surgical simulation system that uses preoperative CT to model individualized lung deformation during thoracic surgery. Introduced in 2020, RPM has been applied clinically as a novel surgical guide, demonstrating its translational impact. With a career spanning over two decades, Nakao’s work bridges engineering and clinical practice, offering tangible tools to improve surgical outcomes and training.
Research Focus
Key Achievements
Top Papers
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- 3A Haptic Navigation System for Supporting Master-Slave Robotic Surgery6 citations · 2003
- 4