Current status and limitations of artificial intelligence in colonoscopy.

Adenoma / diagnostic imaging Artificial Intelligence / standards Clinical Trials as Topic Colonic Neoplasms / diagnostic imaging Colonic Polyps / diagnostic imaging Colonoscopy / methods Deep Learning Diagnosis, Computer-Assisted Humans Precancerous Conditions / diagnostic imaging Quality Improvement Randomized Controlled Trials as Topic
colonic polyps colonoscopy colorectal neoplasms computer-assisted deep learning diagnosis endoscopy gastrointestinal

Journal

United European gastroenterology journal
ISSN: 2050-6414
Titre abrégé: United European Gastroenterol J
Pays: England
ID NLM: 101606807

Informations de publication

Date de publication:
06 2021
Historique:
received: 28 11 2020
accepted: 28 02 2021
entrez: 7 10 2021
pubmed: 8 10 2021
medline: 15 1 2022
Statut: ppublish

Résumé

Artificial intelligence (AI) using deep learning methods for polyp detection (CADe) and characterization (CADx) is on the verge of clinical application. CADe already implied its potential use in randomized controlled trials. Further efforts are needed to take CADx to the next level of development. This work aims to give an overview of the current status of AI in colonoscopy, without going into too much technical detail. A literature search to identify important studies exploring the use of AI in colonoscopy was performed. This review focuses on AI performance in screening colonoscopy summarizing the first prospective trials for CADe, the state of research in CADx as well as current limitations of those systems and legal issues.

Sections du résumé

BACKGROUND
Artificial intelligence (AI) using deep learning methods for polyp detection (CADe) and characterization (CADx) is on the verge of clinical application. CADe already implied its potential use in randomized controlled trials. Further efforts are needed to take CADx to the next level of development.
AIM
This work aims to give an overview of the current status of AI in colonoscopy, without going into too much technical detail.
METHODS
A literature search to identify important studies exploring the use of AI in colonoscopy was performed.
RESULTS
This review focuses on AI performance in screening colonoscopy summarizing the first prospective trials for CADe, the state of research in CADx as well as current limitations of those systems and legal issues.

Identifiants

DOI: 10.1002/ueg2.12108 PMID: 34617420 PMC: PMC8259277
pubmed: 34617420
doi: 10.1002/ueg2.12108
pmc: PMC8259277
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

527-533

Commentaires et corrections

Type : CommentIn

Informations de copyright

© 2021 The Authors. United European Gastroenterology Journal published by Wiley Periodicals LLC. on behalf of United European Gastroenterology.

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Auteurs

Alexander Hann (A)

Department of Internal Medicine II, Interventional and Experimental Endoscopy (InExEn), University Hospital Wuerzburg, Würzburg, Germany.
ORCID: 0000-0001-8035-3559

Joel Troya (J)

Department of Internal Medicine II, Interventional and Experimental Endoscopy (InExEn), University Hospital Wuerzburg, Würzburg, Germany.

Daniel Fitting (D)

Department of Internal Medicine II, Interventional and Experimental Endoscopy (InExEn), University Hospital Wuerzburg, Würzburg, Germany.

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