Artificial intelligence and deep learning for small bowel capsule endoscopy.
artificial intelligence
capsule endoscopy
convolutional neural network
deep learning
machine learning
Journal
Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
ISSN: 1443-1661
Titre abrégé: Dig Endosc
Pays: Australia
ID NLM: 9101419
Informations de publication
Date de publication:
Jan 2021
Jan 2021
Historique:
received:
30
07
2020
accepted:
16
11
2020
pubmed:
20
11
2020
medline:
29
7
2021
entrez:
19
11
2020
Statut:
ppublish
Résumé
Capsule endoscopy is ideally suited to artificial intelligence-based interpretation given its reliance on pattern recognition in still images. Time saving viewing modes and lesion detection features currently available rely on machine learning algorithms, a form of artificial intelligence. Current software necessitates close human supervision given poor sensitivity relative to an expert reader. However, with the advent of deep learning, artificial intelligence is becoming increasingly reliable and will be increasingly relied upon. We review the major advances in artificial intelligence for capsule endoscopy in recent publications and briefly review artificial intelligence development for historical understanding. Importantly, recent advancements in artificial intelligence have not yet been incorporated into practice and it is immature to judge the potential of this technology based on current platforms. Remaining regulatory and standardization hurdles are being overcome and artificial intelligence-based clinical applications are likely to proliferate rapidly.
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
290-297Informations de copyright
© 2020 Japan Gastroenterological Endoscopy Society.
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