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AMIA Jt Summits Transl Sci Proc. 2019 May 6;2019:453-461. eCollection 2019.

Duodenal Biopsies Classification and Understanding using Convolutional Neural Networks.

Author information

1
Systems and Information Engineering, University of Virginia, Charlottesville, Virginia, United States.
2
Division of Gastroenterology, Hepatology, & Nutrition, Department of Pediatrics, University of Virginia, Charlottesville, Virginia, United States.
3
Department of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan.

Abstract

Environmental Enteropathy (EE) and celiac disease (CD) are gastrointestinal conditions that adversely impact the growth of children. EE is prevalent in low- and middle-income countries, whereas as CD is prevalent worldwide. The histologic appearance of duodenal EE biopsies significantly overlaps with celiac enteropathy. We propose a convolutional neural network (ConvNet) to classify EE cases from Pakistani infants along with celiac and healthy controls from the United States. We also identified areas of biopsies that generate high activation values in the ConvNet model. The identified features helped in distinguishing EE and celiac from healthy intestinal tissues. This work advances the understanding of both diseases and provides a potential screening and diagnostic tool for practitioners.

PMID:
31258999
PMCID:
PMC6568096

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