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Overview of Deep Learning in Gastrointestinal Endoscopy.
- Source :
-
Gut & Liver . Jul2019, Vol. 13 Issue 4, p388-393. 6p. - Publication Year :
- 2019
-
Abstract
- Artificial intelligence is likely to perform several roles currently performed by humans, and the adoption of artificial intelligence-based medicine in gastroenterology practice is expected in the near future. Medical image-based diagnoses, such as pathology, radiology, and endoscopy, are expected to be the first in the medical field to be affected by artificial intelligence. A convolutional neural network, a kind of deeplearning method with multilayer perceptrons designed to use minimal preprocessing, was recently reported as being highly beneficial in the field of endoscopy, including esophagogastroduodenoscopy, colonoscopy, and capsule endoscopy. A convolutional neural network-based diagnostic program was challenged to recognize anatomical locations in esophagogastroduodenoscopy images, Helicobacter pylori infection, and gastric cancer for esophagogastroduodenoscopy; to detect and classify colorectal polyps; to recognize celiac disease and hookworm; and to perform small intestine motility characterization of capsule endoscopy images. Artificial intelligence is expected to help endoscopists provide a more accurate diagnosis by automatically detecting and classifying lesions; therefore, it is essential that endoscopists focus on this novel technology. In this review, we describe the effects of artificial intelligence on gastroenterology with a special focus on automatic diagnosis, based on endoscopic findings. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19762283
- Volume :
- 13
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- Gut & Liver
- Publication Type :
- Academic Journal
- Accession number :
- 137366834
- Full Text :
- https://doi.org/10.5009/gnl18384