Artificial Intelligence Enhances Studies on Inflammatory Bowel Disease

Guihua Chen,Jun Shen

doi:10.3389/fbioe.2021.635764

Abstract

Inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn’s disease (CD), is an idiopathic condition related to a dysregulated immune response to commensal intestinal microflora in a genetically susceptible host. As a global disease, the morbidity of IBD reached a rate of 84.3 per 100,000 persons and reflected a continued gradual upward trajectory. The medical cost of IBD is also notably extremely high. For example, in Europe, it has €3,500 in CD and €2,000 in UC per patient per year, respectively. In addition, taking into account the work productivity loss and the reduced quality of life, the indirect costs are incalculable. In modern times, the diagnosis of IBD is still a subjective judgment based on laboratory tests and medical images. Its early diagnosis and intervention is therefore a challenging goal and also the key to control its progression. Artificial intelligence (AI)-assisted diagnosis and prognosis prediction has proven effective in many fields including gastroenterology. In this study, support vector machines were utilized to distinguish the significant features in IBD. As a result, the reliability of IBD diagnosis due to its impressive performance in classifying and addressing region problems was improved. Convolutional neural networks are advanced image processing algorithms that are currently in existence. Digestive endoscopic images can therefore be better understood by automatically detecting and classifying lesions. This study aims to summarize AI application in the area of IBD, objectively evaluate the performance of these methods, and ultimately understand the algorithm–dataset combination in the studies.

Highlights

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder with soaring incidences recorded worldwide in recent years
This study shows that machine learning (ML) can remedy some inherent limitations of genome-wide association studies (GWAS) and can analyze the GWAS data
This computer-aided diagnosis (CAD) system constructed by 312 features on the endoscopic image showed its potential to fully automated the identification of histological inflammation related to ulcerative colitis (UC) with 74% diagnostic sensitivity, 97% specificity, and 91% accuracy (Figure 3; Maeda et al, 2019)

Summary

Introduction

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder with soaring incidences recorded worldwide in recent years. Several studies have drawn attention to the similarity between the current prevalence of IBD in newly industrialized countries, with anterior patterns observed in the Western world. The two major manifestations of IBD, namely, ulcerative colitis (UC) and Crohn’s disease (CD), are recognized as a result of complex interactions between genetic and environmental factors. A recent genetic association study identified 163 susceptibility loci for IBD. In this case, the impact of host–microbe interactions in pathogenesis was scrutinized due to the link between these susceptibility loci and the microbial response (Liu et al, 2015). The intestinal microecology has been a focal point for recent studies (Nishida et al, 2018; Yue et al, 2019)

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Conclusion