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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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Meaning of Cystoscopic Examination:Cystoscopy is an essential diagnostic tool in urology that is used to assess the structure and function of the genitourinary system. It provides a direct view of the urethra, bladder, and, in some cases, the ureteral openings. This procedure helps detect structural abnormalities, infections, cancers, and blockages in the urinary tract. There are two types of cystoscopy:Flexible cystoscopy is commonly performed in outpatient settings due to its less invasive...
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The colon, or large intestine, is the final segment of the digestive system. Its primary functions include absorbing water and vitamins produced by gut bacteria and transforming waste from liquid to solid to form stool. In adults, the large intestine is approximately 5 feet long and consists of four main sections:
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Related Experiment Video

Updated: Nov 4, 2025

Murine Endoscopy for In Vivo Multimodal Imaging of Carcinogenesis and Assessment of Intestinal Wound Healing and Inflammation
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Kvasir-Capsule, a video capsule endoscopy dataset.

Pia H Smedsrud1,2,3, Vajira Thambawita4,5, Steven A Hicks4,5

  • 1SimulaMet, Oslo, Norway. pia@simula.no.

Scientific Data
|May 28, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces Kvasir-Capsule, a large dataset for video capsule endoscopy (VCE) to advance artificial intelligence (AI) in detecting gastrointestinal anomalies. The dataset aids in developing AI algorithms to improve VCE diagnostic accuracy.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Gastroenterology

Background:

  • Artificial intelligence (AI) shows promise for enhancing video capsule endoscopy (VCE) by improving anomaly detection and reducing manual workload.
  • Challenges in VCE research include sparse medical data and limited expert time for data annotation.

Purpose of the Study:

  • To introduce Kvasir-Capsule, a comprehensive dataset designed to facilitate the development of advanced AI algorithms for VCE.
  • To support research in computer-assisted diagnosis for VCE by providing a large, annotated dataset.

Main Methods:

  • The Kvasir-Capsule dataset was curated from 117 VCE videos, yielding over 4.7 million image frames.
  • A subset of 47,238 frames was meticulously labelled with bounding boxes for 14 distinct findings, verified by medical experts.
  • The dataset includes 4,694,266 unlabelled frames, offering extensive data for algorithm training.

Main Results:

  • The Kvasir-Capsule dataset comprises a significant volume of VCE imagery, including a substantial number of annotated frames.
  • This resource enables the training and validation of AI models for improved VCE analysis.

Conclusions:

  • The Kvasir-Capsule dataset is a valuable resource for advancing AI in VCE.
  • It has the potential to significantly contribute to the development of more accurate and efficient VCE diagnostic tools.