Takuto Mikada

Papers

Total Citations

H-Index

About

Takuto Mikada is a leading researcher at the intersection of robotic surgery and artificial intelligence, with a primary focus on human–robot cooperative control and computer-assisted surgical systems. His most impactful work, "Suturing Support by Human Cooperative Robot Control Using Deep Learning" (2020, 19 citations), addresses a critical challenge in the field: automating surgical tasks while accommodating individual patient variability. Rather than pursuing full autonomy, Mikada proposes a deep learning–driven cooperative control framework that allows surgeons and robots to work in tandem, enhancing precision and safety during suturing. This human-in-the-loop approach represents a significant contribution to the practical deployment of surgical robotics. In a related study, "Three-dimensional posture estimation of robot forceps using endoscope with convolutional neural network" (2020, 7 citations), he tackles the problem of sensorless instrument tracking. By employing convolutional neural networks to estimate the 3D posture of forceps directly from endoscopic images, Mikada offers a washable, cost-effective alternative to traditional sensor-based systems. His work is foundational for developing intuitive, image-guided robotic assistants that can seamlessly integrate into existing surgical workflows, marking him as a rising innovator in intelligent surgical technology.