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Deep learning-based gastrocolic trunk recognition in laparoscopic right hemicolectomy.

Masahiro Fuse1,2,3, Daichi Kitaguchi1,2, Norihito Kosugi1

  • 1Medical Device Innovation Office, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.

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|September 2, 2025
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Summary
This summary is machine-generated.

A new deep learning model can identify the gastrocolic trunk in laparoscopic right hemicolectomy videos, potentially improving surgical safety and reducing bleeding risks during colon cancer surgery.

Keywords:
Artificial intelligenceDeep learningGastrocolic trunkLaparoscopyRight hemicolectomy

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Area of Science:

  • Surgical innovation in oncology
  • Artificial intelligence in medical imaging
  • Minimally invasive surgery techniques

Background:

  • Intraoperative bleeding from the gastrocolic trunk is a significant risk during laparoscopic right hemicolectomy for colon cancer.
  • Accurate identification of the gastrocolic trunk is essential for preventing surgical complications.

Purpose of the Study:

  • To develop and evaluate a deep learning model for automatic recognition of the gastrocolic trunk in surgical videos.
  • To enhance surgical safety in laparoscopic right hemicolectomy by providing real-time anatomical identification.

Main Methods:

  • A deep learning model was developed using annotated still images from laparoscopic right hemicolectomy videos.
  • The model performed segmentation tasks to distinguish the gastrocolic trunk and superior mesenteric vein.
  • Data from 43 patients across 10 Japanese hospitals were analyzed using fivefold cross-validation.

Main Results:

  • The deep learning model achieved an average Dice coefficient of 0.84 for vein segmentation (Task A).
  • Segmentation of the gastrocolic trunk yielded average Dice coefficients of 0.61 (Task B) and 0.58 (Task C).

Conclusions:

  • A deep learning model was successfully developed to identify and visualize the gastrocolic trunk during laparoscopic right hemicolectomy.
  • This technology holds promise for improving procedural safety and reducing intraoperative bleeding risks.
  • Further clinical validation is needed to confirm the model's efficacy in real-world surgical settings.