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External Validation of an Automated Surgical Step Recognition Model for Robotic Distal Gastrectomy (RDG) Using a

James S Strong1,2, Masahiro Yura3, Masashi Takeuchi1

  • 1Department of Surgery Keio University School of Medicine Tokyo Japan.

Annals of Gastroenterological Surgery
|November 7, 2025
PubMed
Summary

An artificial intelligence (AI) model for robotic distal gastrectomy (RDG) step recognition achieved 86% accuracy when trained on multi-institutional data. This demonstrates AI

Keywords:
artificial intelligenceautomated phase recognitionrobotic distal gastrectomy

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Area of Science:

  • Surgical innovation
  • Artificial intelligence in medicine
  • Computer vision in healthcare

Background:

  • Artificial intelligence (AI) is transforming surgical practices, improving the analysis and outcomes of complex procedures.
  • AI-based computer vision has led to the development of a novel AI model for recognizing steps in robotic distal gastrectomy (RDG).

Purpose of the Study:

  • To develop and assess a novel AI model for recognizing surgical steps in robotic distal gastrectomy (RDG).
  • To evaluate the impact of multi-institutional versus single-institution training data on the AI model's performance.

Main Methods:

  • The study analyzed 130 robotic surgical videos from two institutions.
  • A multi-stage temporal convolutional network (TeCNO) was employed for the AI model, trained on surgeon-annotated videos.
  • Performance was evaluated using accuracy, precision, recall, and F-value metrics.

Main Results:

  • AI models trained on single institutions achieved moderate accuracy (56%-63%) in predicting RDG steps.
  • Training the AI model on multi-institutional data significantly improved step recognition accuracy to 86%.
  • Results were validated using F-scores and precision tests.

Conclusions:

  • An AI step recognition model for RDG can predict surgical steps in external datasets with moderate accuracy.
  • Training AI models on multi-institutional datasets substantially enhances their step recognition capabilities.
  • Diverse, multi-institutional training data is crucial for developing precise AI models applicable across different institutions.