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Deep learning-based surgical step recognition for laparoscopic right-sided colectomy.

Ryoya Honda1,2, Daichi Kitaguchi3, Yuto Ishikawa1

  • 1Department for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, Chiba, Japan.

Langenbeck'S Archives of Surgery
|October 17, 2024
PubMed
Summary
This summary is machine-generated.

This study developed a deep-learning model to automatically recognize surgical steps in laparoscopic right-sided colectomy (LAP-RC). The model achieved high accuracy, aiding in standardizing this complex procedure.

Keywords:
Convolutional neural networkDeep learningLaparoscopic right-sided colectomyPhase recognitionReal-time automatic recognitionStandardization

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Area of Science:

  • Surgical innovation
  • Medical imaging analysis
  • Artificial intelligence in medicine

Background:

  • Laparoscopic right-sided colectomy (LAP-RC) involves complex surgical steps requiring standardization.
  • Deep learning (DL) offers potential for analyzing surgical procedures and improving standardization.

Purpose of the Study:

  • To develop a DL-based computer vision model for recognizing surgical steps in LAP-RC.
  • To evaluate the performance of the developed step recognition model.

Main Methods:

  • Retrospective analysis of 78 LAP-RC videos (laparoscopic ileocecal resection and laparoscopic right-sided hemicolectomy).
  • Videos were divided into images and used to train a DL model for classifying eight or five surgical steps.
  • Performance was evaluated using precision, recall, F1 scores, and overall accuracy.

Main Results:

  • The DL model achieved an overall accuracy of 72.1% for eight-step classification and 82.9% for a simplified five-step classification.
  • The study included 35 LAP-ICR and 44 LAP-RHC procedures.

Conclusions:

  • The developed DL model demonstrates effective performance in recognizing surgical steps during LAP-RC.
  • Automated surgical step recognition can contribute to standardizing LAP-RC procedures.