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Review
. 2022 Apr 8;12(4):927.
doi: 10.3390/diagnostics12040927.

Artificial Intelligence in Digestive Endoscopy-Where Are We and Where Are We Going?

Radu-Alexandru Vulpoi  1 , Mihaela Luca  2 , Adrian Ciobanu  2 , Andrei Olteanu  1 , Oana-Bogdana Barboi  1 , Vasile Liviu Drug  1
Affiliations
Review

Artificial Intelligence in Digestive Endoscopy-Where Are We and Where Are We Going?

Radu-Alexandru Vulpoi et al. Diagnostics (Basel). .

Abstract

Artificial intelligence, a computer-based concept that tries to mimic human thinking, is slowly becoming part of the endoscopy lab. It has developed considerably since the first attempt at developing an automated medical diagnostic tool, today being adopted in almost all medical fields, digestive endoscopy included. The detection rate of preneoplastic lesions (i.e., polyps) during colonoscopy may be increased with artificial intelligence assistance. It has also proven useful in detecting signs of ulcerative colitis activity. In upper digestive endoscopy, deep learning models may prove to be useful in the diagnosis and management of upper digestive tract diseases, such as gastroesophageal reflux disease, Barrett's esophagus, and gastric cancer. As is the case with all new medical devices, there are challenges in the implementation in daily medical practice. The regulatory, economic, organizational culture, and language barriers between humans and machines are a few of them. Even so, many devices have been approved for use by their respective regulators. Future studies are currently striving to develop deep learning models that can replicate a growing amount of human brain activity. In conclusion, artificial intelligence may become an indispensable tool in digestive endoscopy.

Keywords: artificial intelligence; computer-aided detection; computer-aided diagnosis; deep learning; digestive endoscopy.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Assigning colors to different components in a colonoscopy frame by deep learning semantic segmentation: mucosa in red, residue in yellow and reflections in blue.
Figure 2
Figure 2
Examples of deep learning colonoscopy detection in real-time on an NVIDIA Jetson Xavier microsystem.
See this image and copyright information in PMC

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