Tomoaki Terakawa

Papers

Total Citations

H-Index

About

Tomoaki Terakawa is a pioneering researcher at the intersection of artificial intelligence and robotic surgery, with a primary focus on developing intelligent systems for urological procedures. His most significant contribution is the creation of a convolutional neural network (CNN) model using EfficientNet B7 for real-time surgical phase recognition during robot-assisted laparoscopic radical prostatectomy (RARP). This work, published in 2025, has already garnered 3 citations, demonstrating its immediate relevance to the field. Terakawa’s research emphasizes model interpretability and cross-platform validation, ensuring that AI tools are not only accurate but also transparent and adaptable across different surgical systems. By enabling automated phase detection, his work promises to enhance surgical training, improve workflow efficiency, and reduce errors in complex procedures. His contributions are particularly notable for bridging the gap between cutting-edge deep learning and practical clinical applications, positioning him as a key figure in the emerging field of AI-assisted surgery. Terakawa’s research holds the potential to transform how surgeons interact with robotic platforms, making operations safer and more standardized.