Endoscopy is one of the cornerstones in the field of gastroenterology. The original
fiberoptic endoscope was developed in the 1950s. From this point in time and decade
after decade the field of endoscopy continues to this day to grow and evolve. Endoscopic
retrograde cholangiography was developed in the 1970s and EUS in the 1980s, further
showing the potential of endoscopy to have no boundaries. The image quality of the
scope is now high-definition white light along with optical enhancements such as narrow-band
imaging (NBI), with the goal to improve mucosal surface area inspection to both identify
and interpret abnormal areas. Outside of medicine, we have seen the growth of artificial
intelligence (AI) in our daily lives, from Waze for navigation, smartphones, and,
most recently, self-driving cars. It was only a matter of time before AI would enter
the GI arena in the area of endoscopy. AI applications in medicine have occurred in
ophthalmology and dermatology along with other areas. In endoscopy, AI started out
in colonoscopy to help improve polyp detection and adenoma detection and to interpret
the lesion patterns, differentiating between benign and precancerous polyps.
Abbreviations:
ADR (adenoma detection rate), AI (artificial intelligence), AUC (area under the curve), BE (Barrett’s esophagus), CAD (computer-aided diagnosis), CNN (convoluted neural network), NBI (narrow-band imaging), NPV (negative predictive value), PPV (positive predictive value), VLE (volumetric laser endomicroscopy), WCE (wireless capsule endoscopy)To read this article in full you will need to make a payment
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References
- Artificial intelligence-assisted polyp detection for colonoscopy: initial experience.Gastroenterology. 2018; 154: 2027-2029
- Artificial intelligence for real-time multiple polyp detection with identification, tracking, and optical biopsy during colonoscopy [abstract].Gastroenterology. 2019; 156: S48-9
- Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study.Gut. 2019; 68: 1813-1819
- Automated polyp detection in the colorectum: a prospective study (with videos).Gastrointest Endosc. 2019; 89: 576-582
- Will computer-aided detection and diagnosis revolutionize colonoscopy?.Gastroenterology. 2017; 153: 1460-1464
- Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model.Gut. 2017; 68: 94-100
- Artificial intelligence image classifier based on nonoptical magnified images accurately predicts histology and endoscopic resectability of different colonic lesions.Gastroenterology. 2019; 156: S48
- Computer-aided diagnosis for small colorectal lesions: a multi-center validation “endobrain study” designed to obtain regulatory approval.Gastrointest Endosc. 2019; 89: AB76
- Real-time use of artificial intelligence in identification of diminutive polyps during colonoscopy.Ann Inter Med. 2018; 169: 357
- Machine learning creates a simple endoscopic classification system that improves dysplasia detection in Barrett’s oesophagus amongst non-expert endoscopists.Gastroenterol Res Pract. 2018; 65: A283
- Computer-aided detection of early neoplastic lesions in Barrett’s esophagus: towards a supportive detection system in endoscopy.Gastroenterology. 2016; 48: 617-624
- Deep learning algorithm for characterization of barretts neoplasia demonstrates high accuracy on NBI-zoom images.Gastroenterology. 2019; 156: S58
- Endoscopic surveillance of Barrett’s esophagus using volumetric laser endomicroscopy with artificial intelligence image enhancement.Gastroenterology. 2019; 157: 303-305
- Computer-aided detection of early Barrett’s neoplasia using volumetric laser endomicroscopy.Gastrointest Endosc. 2017; 86: 839-846
- Interpretation of volumetric laser endomicroscopy in Barrett’s esophagus using image enhancement software.Dis Esophagus. 2019; 32: doz037
- Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks.Gastrointest Endosc. 2019; 89: 25-32
- Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy.Gut. 2019; 68: 2161-2169
- A deep neural network improves endoscopic detection of early gastric cancer without blind spots.Endoscopy. 2019; 51: 522-531
- Unsupervised machine learning based automatic demarcation line drawing system on NBI images of early gastric cancer.Gastroenterology. 2019; 156: S-937
- Comparing artificial intelligence using deep learning through convolutional neural networks and endoscopist’s diagnostic ability for detecting early gastric cancer [abstract].Gastrointest Endosc. 2019; 89: AB75
- Application of convolutional neural networks for automated ulcer detection in wireless capsule endoscopy images.Sensors. 2019; 19: 1265
- A neural network algorithm for detection of GI angiectasia during small-bowel capsule endoscopy.Gastrointest Endosc. 2018; 89: 189-194
- Application of machine learning and artificial intelligence in the detection of dysplasia in intraductal papillary mucinous neoplasms using EUS-guided needle-based confocal laser endomicroscopy [abstract].Gastroenterology. 2019; 156: S938
- Use of artificial intelligence to predict future risk for improved colon cancer screening intervals.Gastroenterology. 2014; 146: S734-5
- More on endoscopy databases.Gastrointest Endosc. 2000; 51: 759-761
- Deep learning for automatic generation of endoscopy reports [abstract].Gastrointest Endosc. 2019; 89: AB77
Article info
Publication history
Published online: December 21, 2019
Accepted:
December 15,
2019
Received:
August 28,
2019
Footnotes
DISCLOSURE: The following authors disclosed financial relationships: P. Sharma: Consultant for Olympus, grant support from Erne, Medtronic, Ironwood, and US endoscopy. S. A. Gross: Consultant for Olympus. All other authors disclosed no financial relationships.
Identification
Copyright
© 2020 by the American Society for Gastrointestinal Endoscopy
