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Related Experiment Video

Updated: Jul 5, 2025

Intraoperative Gastroscopy for Tumor Localization in Laparoscopic Surgery for Gastric Adenocarcinoma
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Endoscopic sleeve gastroplasty: stomach location and task classification for evaluation using artificial

James Dials1, Doga Demirel2, Reinaldo Sanchez-Arias3

  • 1Department of Computer Science, Florida Polytechnic University, 4700 Research Way, Lakeland, FL, 33805, USA.

International Journal of Computer Assisted Radiology and Surgery
|January 11, 2024
PubMed
Summary

Automated analysis of endoscopic sleeve gastroplasty (ESG) accurately identifies stomach location and surgical tasks using deep learning. This technology is key for developing automated performance feedback for ESG trainees.

Keywords:
Anatomical localizationArtificial intelligenceData augmentationDeep learningEndoscopic simulatorEndoscopic sleeve gastroplasty

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Area of Science:

  • Gastroenterology
  • Medical Imaging
  • Machine Learning

Background:

  • Objective performance metrics are crucial for endoscopic sleeve gastroplasty (ESG) training.
  • Automating performance measurement in ESG is a primary objective.
  • Accurate identification of surgical tasks and anatomical locations is necessary for automated feedback.

Purpose of the Study:

  • To develop and validate a deep learning model for automated task and location classification during ESG procedures.
  • To assess the performance of image and video analysis for ESG task recognition.
  • To lay the groundwork for automated feedback systems in ESG training.

Main Methods:

  • Utilized a dataset of screenshots and video clips from expert ESG procedures on porcine specimens.
  • Employed data augmentation and synthetic minority oversampling (SMOTE) for dataset balancing.
  • Applied deep learning for image analysis and computer vision for video analysis to classify stomach location and surgical tasks.

Main Results:

  • Image-based stomach localization achieved 97% accuracy with SMOTE.
  • Video-based stomach localization achieved 99% accuracy with SMOTE.
  • Task classification accuracy reached 97% for images and 100% for videos.

Conclusions:

  • The developed models accurately classify stomach regions and surgical tasks during ESG.
  • High accuracy in both image and video analysis demonstrates the potential for automated feedback.
  • This automated system is essential for advancing training and improving endoscopist performance in ESG.