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Z Gastroenterol. 2019 Jun;57(6):767-780. doi: 10.1055/a-0891-4032. Epub 2019 Jun 6.

[Artificial Intelligence in Endoscopy: Deep Neural Nets for Endoscopic Computer Vision - Methods & Perspectives].

[Article in German; Abstract available in German from the publisher]

Author information

Department for Interdisciplinary Endoscopy, University Hospital Hamburg-Eppendorf.
Institute of Anatomy and Experimental Morphology, University Hospital Hamburg-Eppendorf.
DAISYlab, Forschungszentrum Medizintechnik Hamburg, Hamburg, Germany.
Institute of Computational Neuroscience, University Hospital Hamburg-Eppendorf.


in English, German

Artificial neural networks, as a specific approach towards artificial intelligence (AI), can open up a variety of new perspectives for endoscopy, such as automated lesion detection and the precise prediction of a lesion's histology by its endoscopic appearance. Whilst early experiments do suggest an enormous potential for these methods, public expectations on their application in various fields of medicine sometimes appear to be grounded on general fascination rather than detailed understanding of their inner workings. Based on a selective review of the literature, this article shall convey an intuitive understanding of the underlying methods in order to help close the gap between functioning and fascination and allow for a realistic discussion of their perspectives and limitations in endoscopy.After decades of research, the success of deep neuronal networks in image classification has provoked rising interest for AI during recent years. We quickly touch upon the developments surrounding this breakthrough and the reasons for their impact on various disciplines much beyond computer science. Through a comparison with the functioning of the human vision system, we aim to understand the mechanisms of these techniques and their success in computer vision tasks in detail. Based on these considerations, we analyse the functioning of some important AI applications in endoscopy, deduce specific limitations and perspectives, discuss the current state of their evaluation in practical endoscopy and make a plea for the need for additional and realistic tests. Moreover, we seek to give an impression of some further specific applications that can currently be foreseen and how these can shape the role that AI might finally acquire in the routine clinical practice of GI endoscopy.

[Indexed for MEDLINE]

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