Toshihiro Magaribuchi

Papers

Total Citations

H-Index

About

Toshihiro Magaribuchi is a surgeon-researcher at the forefront of integrating artificial intelligence and advanced imaging into robot-assisted urologic surgery. His work centers on two transformative areas: the quantification of "pseudo-haptic feedback" in robotic surgery and the clinical application of 3D navigation for partial nephrectomy. In his most cited work (9 citations, 2024), Magaribuchi pioneered a deep learning method to estimate manipulation forces from standard laparoscopic video feeds, using porcine kidney experiments to make the elusive sensation of pseudo-haptics explicit for surgeons—a critical step toward restoring tactile awareness in haptic-free robotic systems. His complementary research (3 citations, 2024) systematically evaluates the current status and challenges of 3D kidney models derived from preoperative CT scans, addressing how these models can improve surgical precision in small renal cancer treatment. By bridging computer vision, force estimation, and 3D anatomical navigation, Magaribuchi is helping to define the next generation of intelligent, context-aware robotic surgery. His work directly addresses a fundamental limitation of current platforms—the loss of haptic feedback—and offers practical, data-driven solutions that could enhance safety and outcomes in minimally invasive urologic oncology.