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Automated laparoscopic colorectal surgery workflow recognition using artificial intelligence: Experimental research.

Daichi Kitaguchi1, Nobuyoshi Takeshita2, Hiroki Matsuzaki3

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Researchers developed an AI model to automatically identify surgical phases, actions, and tools in laparoscopic colorectal surgery (LCRS) videos. This AI achieved high accuracy, enabling automated video analysis and skill assessment.

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Artificial intelligenceAutomatic video indexingConvolutional neural networkLaparoscopic colorectal surgerySurgical skill assessmentSurgical workflow recognition

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Area of Science:

  • Computer vision
  • Artificial intelligence in medicine
  • Surgical workflow analysis

Background:

  • Automated analysis of laparoscopic surgical videos using AI can streamline manual processes like indexing and skill assessment.
  • Developing AI tools for surgical video analysis requires large, annotated datasets.
  • Laparoscopic colorectal surgery (LCRS) presents unique challenges for automated video interpretation.

Purpose of the Study:

  • To construct a comprehensive annotated dataset of LCRS videos from multiple institutions.
  • To evaluate the accuracy of AI in recognizing surgical phases, actions, and tools within LCRS videos.
  • To facilitate advancements in AI-driven surgical video analysis and applications.

Main Methods:

  • Collected 300 intraoperative LCRS videos from 19 high-volume centers.
  • Annotated over 82 million frames for surgical phase and action classification, and 4000 frames for tool segmentation.
  • Utilized a convolutional neural network (CNN) for video analysis, with Intersection over Union (IoU) for tool recognition evaluation.

Main Results:

  • Achieved 81.0% accuracy for automatic surgical phase classification.
  • Achieved 83.2% accuracy for automatic surgical action classification.
  • Obtained a mean IoU of 51.2% for the automatic segmentation of 5 surgical tools.

Conclusions:

  • A large annotated LCRS video dataset was successfully created.
  • AI demonstrated high accuracy in recognizing surgical phase, action, and tools.
  • The dataset supports applications in automatic video indexing and skill assessment, promoting further AI research.