Hisashi Shinohara
Papers
3
Total Citations
92
H-Index
2
About
Hisashi Shinohara is a pioneering surgeon-researcher at the forefront of integrating artificial intelligence with robotic gastric surgery. His primary research areas include minimally invasive gastrectomy techniques, AI-driven surgical navigation, and innovative reconstructive procedures for gastric cancer. Shinohara’s most impactful contribution is his development of a deep-learning model that automatically segments loose connective tissue fibers (LCTFs) to define safe dissection planes during robot-assisted gastrectomy—a breakthrough that earned 78 citations and promises to augment surgeons’ cognitive skills with real-time anatomical prediction. He has also advanced robotic total gastrectomy for gastric cancer, demonstrating potential advantages in a retrospective cohort study (12 citations), and refined post-proximal gastrectomy reconstruction through a robot-assisted double-flap technique using a knifeless linear stapler, addressing the demanding challenge of preventing gastroesophageal reflux. With a total of 92 citations across his top papers, Shinohara’s work is notable for translating complex surgical challenges into AI-supported solutions, making him a key figure in the evolution of smart, robot-assisted oncology surgery.
Research Focus
Key Achievements
Top Papers
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