Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy01:26

Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy

271
This lesson explores three gastrointestinal imaging techniques: radionuclide testing, colonic transit studies, and virtual colonoscopy.
Radionuclide Testing
Radionuclide testing is a sophisticated medical technique for assessing gastrointestinal motility. It focuses on gastric emptying and colonic transit time. Radioactive markers track the movement of food through the digestive system, providing insights into gastrointestinal disorders.
In gastric emptying studies, a meal's liquid and...
271
Endoscopic Procedures II: Colonoscopy01:25

Endoscopic Procedures II: Colonoscopy

406
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:
406

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Future Global Outlook in Gastrointestinal and Liver Disorders: Consensus and Perspectives from the Leaders of Member Societies of the World Gastroenterology Organization.

Journal of gastrointestinal and liver diseases : JGLD·2026
Same author

Prospective evaluation of artificial intelligence-assisted monitoring of the effective withdrawal time on adenoma detection.

Intestinal research·2026
Same author

Association Between Antibiotic Use for Nongastrointestinal Infections and Inflammatory Bowel Disease Flare-Ups: A Self-Controlled Case Series Study.

The American journal of gastroenterology·2026
Same author

Comparison of Image-Enhanced Endoscopy Techniques for Colorectal Lesion Detection and Characterization: A Network Meta-Analysis of Randomized Controlled Trials.

The American journal of gastroenterology·2025
Same author

Response.

Gastrointestinal endoscopy·2025
Same author

Consumption and expenditure on fidaxomicin and oral vancomycin for Clostridioides difficile infection: A 12-year longitudinal study of 43 countries and regions.

International journal of antimicrobial agents·2025

Related Experiment Video

Updated: Dec 7, 2025

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

962

Is artificial intelligence the final answer to missed polyps in colonoscopy?

Thomas K L Lui1, Wai K Leung2

  • 1Department of Medicine, Queen Mary Hospital, University of Hong Kong, Hong Kong, China.

World Journal of Gastroenterology
|September 30, 2020
PubMed
Summary

Artificial intelligence (AI) shows promise in improving colonoscopy by enhancing polyp detection and reducing miss rates. Further research is needed to address AI model variations and long-term outcomes for real-time application.

Keywords:
AdenomaArtificial intelligenceColonoscopyColorectal cancerPolyps

More Related Videos

Structured Approach to Colonoscopy Technique Optimization: A Single-Center Experience with Novice Endoscopists
03:43

Structured Approach to Colonoscopy Technique Optimization: A Single-Center Experience with Novice Endoscopists

Published on: July 11, 2025

417
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

616

Related Experiment Videos

Last Updated: Dec 7, 2025

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

962
Structured Approach to Colonoscopy Technique Optimization: A Single-Center Experience with Novice Endoscopists
03:43

Structured Approach to Colonoscopy Technique Optimization: A Single-Center Experience with Novice Endoscopists

Published on: July 11, 2025

417
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

616

Area of Science:

  • Gastroenterology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Missed lesions during colonoscopy contribute to post-colonoscopy colorectal cancer.
  • Adenoma miss rates can be as high as 26%, with higher endoscopist detection rates correlating with lower miss rates.

Purpose of the Study:

  • To review the current data on artificial intelligence (AI) for colorectal polyp detection and miss rates.
  • To discuss the principles of various AI models used in colonoscopy.
  • To explore the limitations and future prospects of AI in this field.

Main Methods:

  • Systematic review of current data on AI in colorectal polyp detection.
  • Discussion of the principles behind different AI models.
  • Analysis of AI's impact on polyp miss rates.

Main Results:

  • AI demonstrates accuracy in colorectal polyp detection.
  • AI has the potential to reduce adenoma miss rates.
  • Real-time AI application is currently limited by model heterogeneity and lack of long-term outcome data.

Conclusions:

  • AI is a promising innovation for improving colonoscopy efficacy.
  • Addressing heterogeneity and long-term outcomes is crucial for widespread AI adoption in real-time colonoscopy.
  • Further research is needed to optimize AI for reducing colorectal cancer incidence.