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Related Concept Videos

Endoscopic Procedures III: Video Capsule Endoscopy01:28

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Capsule endoscopy, or wireless or video capsule endoscopy, is a diagnostic procedure for examining the entire gastrointestinal tract. Patients swallow a capsule about the size of a vitamin tablet. The capsule is equipped with a transmitter, a battery, an LED light source, and a color video camera to capture images throughout the gastrointestinal tract. This procedure is particularly useful for diagnosing conditions such as Crohn's disease, ulcerative colitis, tumors, polyps, ulcers,...
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Related Experiment Video

Updated: Nov 28, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Deep learning for surgical phase recognition using endoscopic videos.

Annetje C P Guédon1, Senna E P Meij2, Karim N M M H Osman2

  • 1Department of Clinical Physics, Spaarne Gasthuis, Spaarnepoort 1, 2134TM, Hoofddorp, the Netherlands. aguedon@spaarnegasthuis.nl.

Surgical Endoscopy
|November 25, 2020
PubMed
Summary
This summary is machine-generated.

Deep learning accurately recognizes surgical phases from videos, aiding operating room planning. This technology shows promise for real-time surgical progress monitoring and improved scheduling efficiency.

Keywords:
Automatic recognitionDeep learningEndoscopic videosSurgical phase

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Surgery
  • Surgical Workflow Analysis

Background:

  • Operating room (OR) planning is challenged by inaccurate procedure duration estimates due to significant variations in surgical courses.
  • Real-time, objective, and automatically retrievable information on surgical progress is crucial for adapting daily OR schedules.
  • Endoscopic surgery recordings offer a potential data source for extracting procedural information, with deep learning enabling automated analysis.

Purpose of the Study:

  • To apply state-of-the-art deep learning techniques to endoscopic surgical videos for automated procedure progress recognition.
  • To evaluate the feasibility of this approach concerning performance, scalability, and practical implementation.
  • To assess the potential of deep learning in supporting operating room planning.

Main Methods:

  • A dataset comprising 33 laparoscopic cholecystectomies (LC) and 35 total laparoscopic hysterectomies (TLH) was utilized.
  • Surgical tools and phases within video recordings were annotated.
  • Neural networks were trained on a subset of annotated videos and their performance in recognizing tools and phases was assessed on a separate subset.
  • Scalability tests were conducted, and practical considerations were addressed.

Main Results:

  • The automated recognition of surgical tools and phases achieved an average precision and recall between 0.77 and 0.89.
  • Scalability tests yielded varied outcomes.
  • Legal considerations and significant time investment for dataset annotation were identified as important factors.

Conclusions:

  • Deep learning demonstrates significant potential for automatically extracting information from surgical videos.
  • The findings provide insights into the practical applicability of deep learning techniques for enhancing operating room planning.
  • Further development is needed to address scalability and practical implementation challenges.