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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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Renji endoscopic submucosal dissection video data set for Esophagus.

Jinnan Chen1, Xiangning Zhang2, Tang Cao3

  • 1Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, Shanghai, China.

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Researchers developed the Renji database, a novel collection of esophageal endoscopic submucosal dissection (ESD) videos with detailed phase annotations. This resource aims to advance artificial intelligence in surgical training and analysis.

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Area of Science:

  • Medical Artificial Intelligence
  • Surgical Education Technology
  • Endoscopic Imaging Analysis

Background:

  • Artificial intelligence (AI) significantly enhances surgical training and outcomes.
  • Deep learning for endoscopic procedures requires extensive, annotated datasets.
  • Existing datasets lack comprehensive phase annotations for esophageal procedures.

Purpose of the Study:

  • To introduce the Renji database, the first publicly available collection of esophageal endoscopic submucosal dissection (ESD) videos.
  • To provide detailed, phase-specific annotations for 25 ESD procedures.
  • To establish a benchmark for future esophageal ESD research and AI development.

Main Methods:

  • Collected 25 endoscopic submucosal dissection (ESD) video recordings of esophageal lesions.
  • Developed a specialized annotation framework for phase recognition.
  • A team of expert endoscopists meticulously annotated 141,909 video phases.
  • Made the dataset publicly accessible via the Figshare repository.

Main Results:

  • The Renji database comprises 25 esophageal ESD videos with 141,909 phase annotations.
  • This is the first publicly available dataset of its kind for esophageal ESD.
  • The dataset is freely accessible for research purposes.

Conclusions:

  • The Renji database represents a significant contribution to AI-driven surgical research.
  • It will facilitate the development and validation of AI algorithms for esophageal ESD.
  • This resource aims to improve surgical proficiency and patient outcomes through advanced analytics.