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Related Concept Videos

Endoscopic Procedures IV: Sigmoidoscopy and Laproscopy01:26

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Sigmoidoscopy and laparoscopy are distinct medical procedures that enable physicians to internally inspect different parts of the GI tract. Although they serve different purposes, each is essential for diagnosing and, in some cases, treating various medical conditions.
Sigmoidoscopy
Sigmoidoscopy is a diagnostic procedure that uses a flexible sigmoidoscope equipped with a light source and camera to examine the rectum and sigmoid colon. The procedure involves inserting the tube through the anus...
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Related Experiment Video

Updated: Jan 2, 2026

Surgical Robot-Assisted Transanal Specimen Extraction Radical Sigmoidectomy Without an Auxiliary Abdominal Incision
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Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural

Daichi Kitaguchi1,2,3, Nobuyoshi Takeshita4,5, Hiroki Matsuzaki2

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

Surgical Endoscopy
|December 5, 2019
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Summary

This study developed a deep learning model for automatic surgical phase recognition in laparoscopic sigmoidectomy videos. The model achieved high accuracy in recognizing surgical phases and actions, enabling real-time analysis.

Keywords:
Convolutional neural networkDeep learningLaparoscopic sigmoidectomyPhase recognitionReal-time automatic recognitionSurgical action recognition

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Area of Science:

  • Medical technology
  • Surgical robotics
  • Artificial intelligence in medicine

Background:

  • Automatic surgical workflow recognition is crucial for context-aware computer-assisted surgery (CA-CAS) systems.
  • Previous research has not focused on automatic surgical phase recognition specifically for colorectal surgery.
  • This study addresses the need for automated recognition in laparoscopic procedures.

Purpose of the Study:

  • To develop a deep learning model for automatic surgical phase recognition in laparoscopic sigmoidectomy (Lap-S) videos.
  • To enable real-time surgical phase recognition.
  • To evaluate the accuracy of automatic surgical phase and action recognition using visual data.

Main Methods:

  • Utilized a dataset of 71 Lap-S cases.
  • Processed video data into static images at 1/30s intervals.
  • Manually annotated 11 surgical phases and actions per frame.
  • Developed a Convolutional Neural Network (CNN)-based deep learning model.
  • Validated the model on unseen test data.

Main Results:

  • The model achieved 91.9% accuracy for automatic surgical phase recognition.
  • Extracorporeal action and irrigation recognition accuracy were 89.4% and 82.5%, respectively.
  • The system demonstrated real-time performance at 32 frames per second (fps).
  • High variation in surgical phase duration was observed between cases.

Conclusions:

  • A CNN-based deep learning model successfully recognized surgical phases and actions in Lap-S cases.
  • The system demonstrated high accuracy for both phase and target action recognition.
  • The study confirmed the feasibility of real-time automatic surgical phase recognition at a high frame rate.