Artificial Intelligence Applied to Gastrointestinal Diagnostics: A Review

Abstract

Artificial intelligence (AI), a discipline encompassed by data science, has seen recent rapid growth in its application to healthcare and beyond, and is now an integral part of daily life. Uses of AI in gastroenterology include the automated detection of disease and differentiation of pathology subtypes and disease severity. Although a majority of AI research in gastroenterology focuses on adult applications, there are a number of pediatric pathologies that could benefit from more research. As new and improved diagnostic tools become available and more information is retrieved from them, AI could provide physicians a method to distill enormous amounts of data into enhanced decision-making and cost saving for children with digestive disorders. This review provides a broad overview of AI and examples of its possible applications in pediatric gastroenterology.

Figure 1.. AI and its sub-classifications

Solid arrows represent subtypes of the preceding box (i.e. deep learning is a type of machine learning); dotted arrows represent interconnectedness in applications (i.e. machine learning is used in both natural language processing and computer vision which themselves are much broader fields)

Figure 2.. Duodenal Biopsy Convolutional Neural Network framework

The CNN is comprised of 4 convolution layers and 1 fully connected layer. Each convolution layer has 3 sub-layers (CRMP: convolution, ReLU activation, & max pooling layers). Deconvolution layers are in red, these increase the resolution & find locations of “importance features” within the input image.