Artificial Intelligence Enhances Studies on Inflammatory Bowel Disease

• Published in: Frontiers in bioengineering and biotechnology Vol. 9; p. 635764
• Main Authors: Chen, Guihua, Shen, Jun
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 08.07.2021
• Subjects: artificial intelligence; Bioengineering and Biotechnology; Crohn’s disease; inflammatory bowel disease; machine learning; ulcerative colitis
• ISSN: 2296-4185; 2296-4185
• DOI: 10.3389/fbioe.2021.635764

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.