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Related Experiment Video

Updated: Jun 7, 2025

Application of Hemostatic Devices in Laparoscopic Hepatectomy
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Deep learning-based automatic bleeding recognition during liver resection in laparoscopic hepatectomy.

Taiki Sunakawa1,2,3, Daichi Kitaguchi3, Shin Kobayashi1

  • 1Department of Hepatobiliary and Pancreatic Surgery, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.

Surgical Endoscopy
|November 18, 2024
PubMed
Summary

Researchers developed an automatic bleeding recognition system using deep learning for laparoscopic hepatectomy (LH). This AI tool can identify bleeding regions during liver surgery, potentially improving patient safety and surgical skill assessment.

Keywords:
Intraoperative hemorrhage monitoringLaparoscopic surgery advancementsMachine learning algorithmsReal-time bleeding detectionSurgical assistance systemsSurgical safety enhancements

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Area of Science:

  • Surgical Innovation
  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis

Background:

  • Intraoperative hemorrhage during laparoscopic hepatectomy (LH) is linked to adverse postoperative outcomes.
  • Effective hemostasis is crucial for surgical safety.
  • Current deep learning models for bleeding detection in LH are lacking.

Purpose of the Study:

  • To develop a deep learning model for automatic recognition of bleeding regions during liver transection in LH.
  • To enhance the safety of laparoscopic hepatectomy through improved bleeding detection.

Main Methods:

  • Retrospective feasibility study using LH videos.
  • Image segmentation of bleeding regions at 30 frames per second.
  • Development of a convolutional neural network model for semantic segmentation.
  • Evaluation using precision, recall, and Dice coefficients.

Main Results:

  • Utilized 2203 annotated images from 44 LH videos.
  • Model achieved precision of 0.76, recall of 0.79, and Dice coefficient of 0.77.
  • Demonstrated feasibility of deep learning for bleeding region identification.

Conclusions:

  • An automatic bleeding recognition model using semantic segmentation was successfully developed and validated.
  • The model shows potential for real-time intraoperative alerts.
  • Future applications include surgical skill evaluation in LH procedures.