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About

Nao Kobayashi is pioneering the integration of artificial intelligence into minimally invasive surgery, with a focus on robot-assisted gastrectomy, rectal cancer surgery, and urological procedures. His research centers on developing deep-learning models that automatically segment critical anatomical structures—such as loose connective tissue fibers, nerves, the pancreas, and the ureter—to define safe dissection planes and enhance surgical precision. Kobayashi’s most cited work (2021, 78 citations) introduced an AI-driven segmentation method for loose connective tissue fibers, directly supporting surgeons’ cognitive skills during gastrectomy. He has since extended this approach to nerve recognition in rectal cancer surgery (2024, 22 citations) and real-time ureter segmentation during robot-assisted cystectomy (2025). Notably, his cluster quasirandomized controlled trial (2025) demonstrated the effectiveness of AI-based visualization as an educational tool, bridging surgical training and intraoperative guidance. With a growing body of work that consistently integrates semantic segmentation and convolutional neural networks, Kobayashi is shaping the future of AI-guided fluorescence-like navigation, making complex procedures safer and more teachable.