  Celiac Disease (CD) and Environmental Enteropathy (EE) are common causes of
malnutrition and adversely impact normal childhood development. CD is an
autoimmune disorder that is prevalent worldwide and is caused by an increased
sensitivity to gluten. Gluten exposure destructs the small intestinal
epithelial barrier, resulting in nutrient mal-absorption and childhood
under-nutrition. EE also results in barrier dysfunction but is thought to be
caused by an increased vulnerability to infections. EE has been implicated as
the predominant cause of under-nutrition, oral vaccine failure, and impaired
cognitive development in low-and-middle-income countries. Both conditions
require a tissue biopsy for diagnosis, and a major challenge of interpreting
clinical biopsy images to differentiate between these gastrointestinal diseases
is striking histopathologic overlap between them. In the current study, we
propose a convolutional neural network (CNN) to classify duodenal biopsy images
from subjects with CD, EE, and healthy controls. We evaluated the performance
of our proposed model using a large cohort containing 1000 biopsy images. Our
evaluations show that the proposed model achieves an area under ROC of 0.99,
1.00, and 0.97 for CD, EE, and healthy controls, respectively. These results
demonstrate the discriminative power of the proposed model in duodenal biopsies
classification.
