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Current Gastroenterology Reports
Erschienen in:

02.05.2023

Artificial Intelligence in Colonoscopy

verfasst von: Nabil M. Mansour

Erschienen in: Current Gastroenterology Reports | Ausgabe 6/2023

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Abstract

Purpose of Review

Artificial intelligence (AI) is a rapidly growing field in gastrointestinal endoscopy, and its potential applications are virtually endless, with studies demonstrating use of AI for early gastric cancer, inflammatory bowel disease, Barrett’s esophagus, capsule endoscopy, as well as other areas in gastroenterology. Much of the early studies and applications of AI in gastroenterology have revolved around colonoscopy, particularly with regards to real-time polyp detection and characterization. This review will cover much of the existing data on computer-aided detection (CADe), computer-aided diagnosis (CADx), and briefly discuss some other interesting applications of AI for colonoscopy, while also considering some of the challenges and limitations that exist around the use of AI for colonoscopy.

Recent Findings

Multiple randomized controlled trials have now been published which show a statistically significant improvement when using AI to improve adenoma detection and reduce adenoma miss rates during colonoscopy. There is also a growing pool of literature showing that AI can be helpful for characterizing/diagnosing colorectal polyps in real time. AI has also shown promise in other areas of colonoscopy, including polyp sizing and automated measurement and monitoring of quality metrics during colonoscopy.

Summary

AI is a promising tool that has the ability to shape the future of gastrointestinal endoscopy, with much of the early data showing significant benefits to use of AI during colonoscopy. However, there remain several challenges that may delay or hamper the widespread use of AI in the field.
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Metadaten
Titel
Artificial Intelligence in Colonoscopy
verfasst von
Nabil M. Mansour
Publikationsdatum
02.05.2023
Verlag
Springer US
Erschienen in
Current Gastroenterology Reports / Ausgabe 6/2023
Print ISSN: 1522-8037
Elektronische ISSN: 1534-312X
DOI
https://doi.org/10.1007/s11894-023-00872-x

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