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Related Experiment Video

Updated: Jun 12, 2025

Robotic Myotomy and Partial Fundoplication for Achalasia
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Artificial intelligence based surgical support for experimental laparoscopic Nissen fundoplication.

Holger Till1, Ciro Esposito2, Chung Kwong Yeung3

  • 1Department of Pediatric and Adolescent Surgery, Medical University of Graz, Graz, Austria.

Frontiers in Pediatrics
|June 9, 2025
PubMed
Summary

This study developed an AI/CV model to classify laparoscopic Nissen fundoplication quality. The model accurately detects visible quality indicators, showing potential for real-time surgical support.

Keywords:
EfficientNetNissen fundoplicationartificial intelligence (AI)computer vision (CV)visual quality indicators (VQIs)

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Area of Science:

  • Artificial intelligence
  • Computer vision
  • Surgical technology

Background:

  • AI/CV shows promise in medical imaging but lags in surgical applications.
  • This study focuses on developing AI for laparoscopic surgery quality assessment.

Purpose of the Study:

  • To develop the first image-based AI/CV model for classifying quality indicators of laparoscopic Nissen fundoplication (LNF).
  • To evaluate the model's performance in distinguishing correct from incorrect surgical configurations.

Main Methods:

  • Six visible quality indicators (VQIs) were defined for Nissen fundoplication.
  • A porcine model was used, generating 57 video sequences and 3,138 images for annotation.
  • Deep learning (EfficientNet) was employed to train image classifiers to detect VQIs.

Main Results:

  • AI/CV models demonstrated strong performance in predicting VQIs, with an average F1-Score of 0.9738.
  • A multi-class classifier showed similar performance, robust to image augmentation.
  • Detection of incomplete and too loose wraps showed a slight decline in predictive power.

Conclusions:

  • AI/CV algorithms can effectively detect VQIs in laparoscopic Nissen fundoplication images.
  • This proof of concept provides experimental evidence for AI/CV in classifying surgical images.
  • Future development could lead to AI-based real-time surgical support to improve outcomes and safety.