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Automatic bleeding detection in laparoscopic surgery based on a faster region-based convolutional neural network.

Surong Hua1, Junyi Gao1, Zhihong Wang2

  • 1Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.

Annals of Translational Medicine
|June 20, 2022
PubMed
Summary

This study introduces an AI model for automatic bleeding point detection in laparoscopic surgery videos. The model accurately identifies bleeding, aiding surgeons in maintaining hemostasis during complex procedures.

Keywords:
Video bleeding point detectionfaster region-based convolutional neural network (RCNN)laparoscopic surgeryoptical flow

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Surgical Technology

Background:

  • Laparoscopic surgery presents challenges in hemostasis, vision, and surgical approach.
  • Accurate bleeding point localization is crucial for rapid hemorrhage control.
  • Current laparoscopic tools lack automatic bleeding tracking capabilities.

Purpose of the Study:

  • To develop an automated system for detecting bleeding points in laparoscopic surgery videos.
  • To enhance surgical safety and efficiency by providing real-time bleeding identification.
  • To create a tool that assists surgeons in managing hemostasis during procedures.

Main Methods:

  • A spatiotemporal hybrid model based on the Faster Region-based Convolutional Neural Network (Faster RCNN) was developed.
  • The model integrates red-green-blue (RGB) frames and optical flow maps for feature extraction.
  • Training data comprised 12 laparoscopic videos with bleeding events.

Main Results:

  • The hybrid model demonstrated effective bleeding point detection.
  • Performance metrics included a precision rate of 0.8373 and a recall rate of 0.8034.
  • The model successfully detected both arterial and venous bleeding, outperforming single-feature models.

Conclusions:

  • The proposed Faster RCNN-based model shows significant potential for real-time bleeding point location and recognition.
  • This technology can assist surgeons in maintaining and re-establishing hemostasis during laparoscopic operations.
  • The integration of spatiotemporal features improves the accuracy of bleeding detection in surgical videos.