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Related Experiment Video

Updated: Jul 12, 2025

Fluorescent Laparoscopic Central Hepatectomy for Liver Cancer Using Indocyanine Green Negative Staining
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Color-coded laparoscopic liver resection using artificial intelligence: A preliminary study.

Yoshihiko Tashiro1, Takeshi Aoki1, Nao Kobayashi2

  • 1Department of General and Gastroenterological Surgery, Showa University School of Medicine, Tokyo, Japan.

Journal of Hepato-Biliary-Pancreatic Sciences
|October 25, 2023
PubMed
Summary

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Artificial intelligence precisely identifies liver blood vessels during surgery using real-time color coding. This technology enhances navigation, potentially making laparoscopic and robotic liver resections safer.

Area of Science:

  • Surgical Technology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Laparoscopic liver resections require precise identification of intrahepatic vascular structures.
  • Minimizing risks like bleeding during surgery is crucial for patient safety.
  • Real-time intraoperative guidance systems can improve surgical outcomes.

Purpose of the Study:

  • To evaluate the capability of an artificial intelligence (AI) system in identifying intrahepatic vascular structures during laparoscopic liver resection.
  • To assess the real-time performance of the AI system under various imaging conditions, including bleeding and indocyanine green (ICG) fluorescence.

Main Methods:

  • Development and implementation of an AI system for real-time image analysis.
  • Integration of the AI system with laparoscopic surgical equipment and ICG fluorescent imaging.
Keywords:
AI navigation surgeryartificial intelligencecolor-coded surgeryintrahepatic vascular structurelaparoscopic liver resection

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  • Testing the AI system's accuracy in identifying vascular structures during simulated and actual surgical procedures.

    • Main Results:

    • The AI system accurately identified intrahepatic vascular structures in real time.
    • Color-coded visualization of vascular anatomy was achieved under both standard and fluorescent imaging conditions.
    • The system demonstrated potential for real-time navigation support during surgery.

    Conclusions:

    • AI can precisely identify intrahepatic vascular structures during laparoscopic liver resection.
    • The developed AI system offers real-time navigation and enhances safety in liver surgery.
    • This technology holds promise for improving the precision and safety of both laparoscopic and robotic liver resections.