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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.
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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: Sep 12, 2025

Clinical Application of Single-Surgeon, Three-Port, Laparoscopic Resection for Colorectal Cancer with Natural Orifice Specimen Extraction
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Automatic surgical skill assessment using a task classification model in laparoscopic sigmoidectomy.

Keisuke Obuchi1,2, Shin Takenaka3, Daichi Kitaguchi1,3

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

Surgical Endoscopy
|August 8, 2025
PubMed
Summary
This summary is machine-generated.

An artificial intelligence (AI) model was developed to assess surgical skill in laparoscopic colorectal cancer surgery by analyzing dissection and exposure times. This AI tool effectively differentiates between proficient and less proficient surgeons based on key performance metrics.

Keywords:
Artificial intelligenceAutomatic surgical skill assessmentDEI classification modelLaparoscopic sigmoidectomy

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Area of Science:

  • Surgical Technology
  • Artificial Intelligence in Medicine
  • Surgical Skill Assessment

Background:

  • The dissection-exposure time ratio is a critical indicator of surgical efficiency and proficiency in laparoscopic colorectal cancer surgery.
  • Objective assessment of surgical skills is essential for improving patient outcomes and surgical training.
  • Current methods for surgical skill assessment can be subjective and time-consuming.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI) model for automatic recognition and analysis of dissection and exposure times.
  • To explore the utility of AI-driven analysis of surgical video data for objective surgical skill assessment.
  • To correlate AI-derived parameters with established measures of surgical proficiency in laparoscopic sigmoidectomy.

Main Methods:

  • Construction of video datasets from laparoscopic sigmoidectomy (Lap-S) procedures.
  • Classification of surgical skill levels into high (+2 SD) and low (-2 SD) proficiency groups based on ESSQS scores.
  • Development of a convolutional neural network-based image classification model to identify and quantify dissection (D), exposure (E), invalid (I), and outside times, calculating D/E ratio and D-E transitions.

Main Results:

  • The AI model achieved high F1 scores for classifying D (0.92), E (0.82), and I (0.74) times.
  • Significant differences were observed in average D and E times between high and low proficiency groups (p < .01).
  • The high proficiency group exhibited a significantly lower mean D-E transition count (204 ± 96 SD) compared to the low proficiency group (405 ± 188 SD; p < .01).

Conclusions:

  • A novel AI model can automatically classify laparoscopic sigmoidectomy videos based on 'DEI' parameters, effectively distinguishing surgical proficiency.
  • The developed AI model shows potential for enhancing surgical quality assessment and improving surgical education.
  • Objective, AI-driven analysis of surgical videos offers a promising avenue for standardized surgical skill evaluation.