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Updated: May 20, 2026

Robotics in Surgery: A Modular Robotic Platform Driven Gastric Wedge Resection

Robotics in Surgery: A Modular Robotic Platform Driven Gastric Wedge Resection

Published on: February 7, 2025

Artificial intelligence-based anatomical recognition improves surgeon decision-making during robotic gastrectomy.

Kenichi Ishibayashi¹, Noriyuki Inaki², Jumpei Ikeda³

¹Department of Gastrointestinal Surgery, Kanazawa University Hospital, 13-1 Takaramachi, Kanazawa, Ishikawa, 920-8641, Japan.

Gastric Cancer : Official Journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association

|May 19, 2026

Summary

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This study developed an AI model to improve anatomical recognition during robotic distal gastrectomy, enhancing surgical decision-making and safety. The AI system successfully supported surgeons, potentially improving patient outcomes.

Area of Science:

Robotic surgery
Artificial intelligence in medicine
Surgical oncology

Background:

AI-based anatomical recognition shows promise for intraoperative support but has limited clinical use.
This study focused on developing an AI model for suprapancreatic lymph node dissection in robotic distal gastrectomy (RDG).

Purpose of the Study:

To develop and evaluate an AI model for enhanced intraoperative decision-making during RDG.
To assess the AI model's utility in improving anatomical recognition and surgical precision.

Main Methods:

A deep learning model was trained and tested on 67 RDG videos to identify key anatomical structures.
Surgeon performance was evaluated in two experiments assessing peritoneal incision selection and common hepatic artery (CHA) identification time.

Keywords:

Artificial intelligence Deep learning Gastrectomy Gastric cancer Robotic surgery

Related Experiment Videos

Last Updated: May 20, 2026

Robotics in Surgery: A Modular Robotic Platform Driven Gastric Wedge Resection

Robotics in Surgery: A Modular Robotic Platform Driven Gastric Wedge Resection

Published on: February 7, 2025

Main Results:

The AI model achieved Intersection over Union (IoU) values ranging from 0.216 to 0.66 for different anatomical structures.
AI assistance significantly reduced unsafe peritoneal incision lines and improved expert scores.
AI assistance decreased the time required for CHA identification by 9.5 seconds.

Conclusions:

The developed AI system effectively supported surgeons' intraoperative decision-making by enhancing anatomical recognition.
The AI model has the potential to improve surgical safety in robotic distal gastrectomy.