What is New in Gastroenterology and Hepatology

What is New in Gastroenterology and Hepatology

Note: This book has been published under Bentham’s FAST TRACK OPEN ACCESS publication option upon the author’s request. The finalized book will be published soon.

Gastroenterology and hepatology represent dynamic fields of study and practice in internal medicine, with numerous innovations manifesting over the last 30 years. What is New in Gastroenterology and ...
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Artificial Intelligence in Gastrointestinal Endoscopy

Pp. 145-153 (9)

DOI: 10.2174/9781681087870121010016

Author(s): Radu Bogdan Mateescu, Theodor Alexandru Voiosu

Abstract

Artificial intelligence (AI) in endoscopy refers to the capacity of computer algorithms using “machine learning” to aid in the detection and characterization of lesions in the digestive tract. The field of AI in endoscopy is expanding at a very rapid pace and, while the potential for development is enormous, the only validated applications currently available in everyday practice are computer-assisted detection and characterization of colonic polyps. The main advantage of machine learning is the capability of analyzing vast quantities of data to detect patterns that are not readily available to the endoscopist, thus theoretically increasing the accuracy of detection and diagnosis of the predefined lesion. However, the current technology is still heavily reliant on adequate image databases which have to be appraised by expert endoscopists before the algorithms can be trained on these datasets. Furthermore, each individual algorithm is trained to answer very specific questions, usually in a binary fashion (i.e. – is the polyp neoplastic or hyperplastic?). Endoscopists need to be aware of the developments in the field, because in the near future such applications as detection and characterization of early esophageal and gastric cancer might also be included in their diagnostic armamentarium. Finally, several ethical and practical questions regarding the implementation of AI-based diagnosis and treatment in everyday practice need to be addressed by the academic and medical community before the large-scale adoption of AI in endoscopy becomes a reality.

Keywords:

Algorithms, Artificial intelligence, Cancer, Colonoscopy, Computerassisted detection, Computer-assisted diagnosis, Deep learning, Endoscopy.