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Artificial intelligence in endoscopy

Published:December 21, 2019DOI:https://doi.org/10.1016/j.gie.2019.12.018
      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)
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