Current status and limitations of artificial intelligence in colonoscopy

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 de...

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Detalles Bibliográficos
Autores principales: Hann, Alexander, Troya, Joel, Fitting, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Endoscopy
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8259277/
https://www.ncbi.nlm.nih.gov/pubmed/34617420
http://dx.doi.org/10.1002/ueg2.12108
Descripción
Sumario: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.

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