Automated classification of celiac disease during upper endoscopy: Status quo and quo vadis.
Journal:
Computers in biology and medicine
Published Date:
Nov 1, 2018
Abstract
A large amount of digital image material is routinely captured during esophagogastroduodenoscopies but, for the most part, is not used for confirming the diagnosis process of celiac disease which is primarily based on histological examination of biopsies. Recently, considerable effort has been undertaken to make use of image material by developing semi- or fully-automated systems to improve the diagnostic workup. Recently, focus was especially laid on developing state-of-the-art deep learning architectures, exploiting the endoscopist's expert knowledge and on making systems fully automated and thereby completely observer independent. In this work, we summarize recent trends in the field of computer-aided celiac disease diagnosis based on upper endoscopy and discuss about recent progress, remaining challenges, limitations currently prohibiting a deployment in clinical practice and future efforts to tackle them.
Authors
Keywords
Algorithms
Automation
Biopsy
Celiac Disease
Decision Making
Deep Learning
Diagnosis, Computer-Assisted
Duodenum
Endoscopy
Gastroscopy
Humans
Image Interpretation, Computer-Assisted
Image Processing, Computer-Assisted
Machine Learning
Neural Networks, Computer
Observer Variation
Pattern Recognition, Automated