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Related Concept Videos

Endoscopic Procedures III: Video Capsule Endoscopy01:28

Endoscopic Procedures III: Video Capsule Endoscopy

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, unexplained...
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Radionuclide Testing
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Related Experiment Video

Updated: May 19, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Improved Efficiency and Lesion Detection in Small Bowel Capsule Endoscopy Using the Open-Source Artificial

Satoshi Miyazono1, Junji Umeno1, Tomohiro Nagasue1,2

  • 1Department of Medicine and Clinical Science Graduate School of Medical Sciences Kyushu University Fukuoka Japan.

DEN Open
|May 18, 2026
PubMed
Summary

Artificial intelligence (AI) significantly enhances small bowel capsule endoscopy (CE) interpretation. The SEE-AI model improves lesion detection sensitivity and reduces reading time, offering a potential new standard of care.

Keywords:
artificial intelligencecapsule endoscopygastrointestinal tractsmall intestinesuspected small‐bowel bleeding

Related Experiment Videos

Last Updated: May 19, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Area of Science:

  • Gastroenterology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Small bowel capsule endoscopy (CE) interpretation is time-consuming and prone to missed lesions.
  • An open-source, pretrained artificial intelligence (AI) model, SEE-AI, was evaluated to improve diagnostic performance.

Purpose of the Study:

  • To assess if SEE-AI improves diagnostic accuracy and interpretation efficiency in small bowel CE compared to conventional reading.
  • To evaluate SEE-AI's performance in suspected small-bowel bleeding (SSBB) cases.

Main Methods:

  • Retrospective analysis of 249 PillCam SB3 examinations using a two-reader crossover design.
  • SEE-AI generated annotated videos; primary endpoints were lesion detection sensitivity (per-lesion and per-patient).
  • Secondary endpoints included specificity, predictive values, accuracy, and reading time; subgroup analysis for SSBB cases.

Main Results:

  • AI-assisted reading showed significantly higher sensitivity than conventional reading (per-lesion: 98.8% vs. 86.4%; per-patient: 99.1% vs. 80.3%).
  • Mean reading time decreased from 17.9 to 13.7 minutes.
  • In SSBB cases, sensitivity for hemorrhagic lesions improved (per-lesion: 98.2% vs. 82.8%; per-patient: 98.6% vs. 73.5%) with reduced reading time.

Conclusions:

  • SEE-AI significantly improves lesion detection and reduces reading time for small bowel CE interpretation.
  • The AI model maintains openness and reproducibility, potentially reducing clinician workload.
  • AI-assisted reading with SEE-AI may become a practical tool and a future standard of care for small bowel CE.