• computer science
• medical imaging

• Colitis, Ulcerative/diagnosis
• Humans
• Neural Networks, Computer
• Deep Learning
• Algorithms

• NTNU Norwegian University of Science and Technology (incl St. Olavs Hospital - Trondheim University Hospital)

• artificial intelligence
• biomarkers
• machine learning
• outcomes
• prediction
• ulcerative colitis

• differentially expressed genes (DEGs)
• feature selection (FS)
• gene ontology
• high-throughput technologies
• inflammatory bowel disease (IBD)
• machine learning (ML)
• pathway enrichment analysis

• D-830 - 714 - 1435 Deanship of Scientific Research, King Abdulaziz University

• artificial intelligence
• artificial neural networks
• diagnosis
• inflammatory bowel disease
• ulcerative colitis

• Genome-scale metabolic model
• N-acetyl-D-mannosamine
• Parabacteroides merdae ATCC 43184
• biomarker selection
• inflammatory bowel disease
• machine learning
• ulcerative colitis

• Animals
• Mice
• Humans
• Colitis, Ulcerative
• Gastrointestinal Microbiome
• Inflammatory Bowel Diseases
• Colitis
• Machine Learning

• Ulcerative colitis (UC)
• cuproptosis
• cuproptosis-related genes (CRGs)
• immune infiltration
• machine learning

• Crohn’s disease
• disease activity.
• inflammatory bowel disease
• machine learning
• ulcerative colitis

• Crohn’s disease
• IBD-associated neoplasia
• artificial intelligence
• automated diagnosis
• inflammatory bowel disease
• ulcerative colitis

• AI prediction
• Biomarker
• IBD
• Signature genes
• XGBoost

• artificial intelligence
• inflammatory bowel disease
• machine learning

• Chronic Disease
• Colitis, Ulcerative
• Crohn Disease
• Humans
• Inflammatory Bowel Diseases/diagnosis
• Intelligence
• Machine Learning

• University of Southampton
• National Institute for Health Research

Background: Various potential effect of drugs on alleviating diseases by regulating intestinal microbiome as well as the pharmaceutical excipients on gut microbiota has been revealed. However, the interaction between them is rarely investigated. Methods: Histological analysis, immunohistochemistry analysis, enzyme-linked immunosorbent assay (ELISA) analysis, RT-qPCR, and 16S rRNA analysis were utilized to explore the effect mechanism of the five excipients including hydroxypropyl methylcellulose (HPMC) F4M, Eudragit (EU) S100, chitosan (CT), pectin (PT), and rheum officinale polysaccharide (DHP) on berberine (BBR) to cure UC. Results: The combined BBR with PT and DHP group exhibited better therapeutic efficacy of UC with significantly increased colon length, and decreased hematoxylin-eosin (H&E) scores than other groups. Furthermore, the expression of tight junction ZO-1 and occludin in colon tissue were upregulated, and claudin-2 was downregulated. Ultimately, the serum content of tumor necrosis (TNF)-α, interleukin (IL)-1β, and IL-6 was decreased. Moreover, the combined BBR with PT significantly promoted the restoration of gut microbiota. The relative abundance of Firmicutes and Lactobacillus was significantly increased by the supplement of PT and DHP, and the relative abundance of Proteobacteria was downregulated. Conclusions: Our study may provide a new perspective that the selection of pharmaceutical excipients could be a crucial factor affecting the drugs’ therapeutic efficiency outcome.

• Animals
• Berberine/metabolism
• Chitosan/pharmacology
• Claudin-2/metabolism
• Colitis/pathology
• Colitis, Ulcerative/chemically induced
• Colitis, Ulcerative/drug therapy
• Colon/metabolism
• Dextran Sulfate/pharmacology
• Disease Models, Animal
• Eosine Yellowish-(YS)
• Excipients/pharmacology
• Gastrointestinal Microbiome
• Hematoxylin/metabolism
• Hematoxylin/pharmacology
• Hematoxylin/therapeutic use
• Humans
• Hypromellose Derivatives/metabolism
• Interleukin-6/metabolism
• Mice
• Mice, Inbred C57BL
• Occludin/metabolism
• Pectins/pharmacology
• RNA, Ribosomal, 16S/genetics
• RNA, Ribosomal, 16S/metabolism

Ulcerative colitis (UC) is a chronic relapsing inflammatory bowel disease with an increasing incidence and prevalence worldwide. The diagnosis for UC mainly relies on clinical symptoms and laboratory examinations. As some previous studies have revealed that there is an association between gene expression signature and disease severity, we thereby aim to assess whether genes can help to diagnose UC and predict its correlation with immune regulation. A total of ten eligible microarrays (including 387 UC patients and 139 healthy subjects) were included in this study, specifically with six microarrays (GSE48634, GSE6731, GSE114527, GSE13367, GSE36807, and GSE3629) in the training group and four microarrays (GSE53306, GSE87473, GSE74265, and GSE96665) in the testing group. After the data processing, we found 87 differently expressed genes. Furthermore, a total of six machine learning methods, including support vector machine, least absolute shrinkage and selection operator, random forest, gradient boosting machine, principal component analysis, and neural network were adopted to identify potentially useful genes. The synthetic minority oversampling (SMOTE) was used to adjust the imbalanced sample size for two groups (if any). Consequently, six genes were selected for model establishment. According to the receiver operating characteristic, two genes of OLFM4 and C4BPB were finally identified. The average values of area under curve for these two genes are higher than 0.8, either in the original datasets or SMOTE-adjusted datasets. Besides, these two genes also significantly correlated to six immune cells, namely Macrophages M1, Macrophages M2, Mast cells activated, Mast cells resting, Monocytes, and NK cells activated (P  <  0.05). OLFM4 and C4BPB may be conducive to identifying patients with UC. Further verification studies could be conducted.

Artificial intelligence (AI) is assuming an increasingly important and central role in several medical fields. Its application in endoscopy provides a powerful tool supporting human experiences in the detection, characterization, and classification of gastrointestinal lesions. Lately, the potential of AI technology has been emerging in the field of inflammatory bowel disease (IBD), where the current cornerstone is the treat-to-target strategy. A sensible and specific tool able to overcome human limitations, such as AI, could represent a great ally and guide precision medicine decisions. Here we reviewed the available literature on the endoscopic applications of AI in order to properly describe the current state-of-the-art and identify the research gaps in IBD at the dawn of 2022.

The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.

• Artificial intelligence
• Machine learning
• Deep learning
• Clinical applications
• Gastroenterology

• Artificial Intelligence
• Barrett Esophagus
• Diagnostic Imaging
• Endoscopy
• Gastroenterology
• Humans

Inflammatory bowel disease (IBD) is a chronic, incurable disease involving the gastrointestinal tract. It is characterized by complex, unclear pathogenesis, increased prevalence worldwide, and a wide spectrum of extraintestinal manifestations and comorbidities. Recognition of IBD remains challenging and delays in disease diagnosis still poses a significant clinical problem as it negatively impacts disease outcome. The main diagnostic tool in IBD continues to be invasive endoscopy. We aimed to create an IBD machine learning prediction model based on routinely performed blood, urine, and fecal tests. Based on historical patients’ data (702 medical records: 319 records from 180 patients with ulcerative colitis (UC) and 383 records from 192 patients with Crohn’s disease (CD)), and using a few simple machine learning classificators, we optimized necessary hyperparameters in order to get reliable few-features prediction models separately for CD and UC. Most robust classificators belonging to the random forest family obtained 97% and 91% mean average precision for CD and UC, respectively. For comparison, the commonly used one-parameter approach based on the C-reactive protein (CRP) level demonstrated only 81% and 61% average precision for CD and UC, respectively. Results of our study suggest that machine learning prediction models based on basic blood, urine, and fecal markers may with high accuracy support the diagnosis of IBD. However, the test requires validation in a prospective cohort.

• Crohn’s disease
• artificial intelligence
• inflammatory bowel disease
• machine learning
• model
• prediction
• ulcerative colitis

• Inflammatory bowel disease
• artificial intelligence
• machine learning
• neural networks

Research of inflammatory bowel disease (IBD) has identified numerous molecular players involved in the disease development. Even so, the understanding of IBD is incomplete, while disease treatment is still far from the precision medicine. Reliable diagnostic and prognostic biomarkers in IBD are limited which may reduce efficient therapeutic outcomes. High-throughput technologies and artificial intelligence emerged as powerful tools in search of unrevealed molecular patterns that could give important insights into IBD pathogenesis and help to address unmet clinical needs. Machine learning, a subtype of artificial intelligence, uses complex mathematical algorithms to learn from existing data in order to predict future outcomes. The scientific community has been increasingly employing machine learning for the prediction of IBD outcomes from comprehensive patient data-clinical records, genomic, transcriptomic, proteomic, metagenomic, and other IBD relevant omics data. This review aims to present fundamental principles behind machine learning modeling and its current application in IBD research with the focus on studies that explored genomic and transcriptomic data. We described different strategies used for dealing with omics data and outlined the best-performing methods. Before being translated into clinical settings, the developed machine learning models should be tested in independent prospective studies as well as randomized controlled trials.

• IBD
• artificial intelligence
• genomics
• prediction modeling
• transcriptomics

• artificial intelligence
• inflammatory bowel diseases
• machine learning
• predictive modeling

With the development of artificial intelligence (AI) and its gradual application in the medical field, AI has brought new ideas to the medical development. The research and application of AI in inflammatory l bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn's disease (CD), are increasing. Selecting appropriate models and methods through machine learning can help diagnose, treat, and predict the prognosis of IBD. In recent years, AI combined with endoscopy has made an appearance in the diagnosis of IBD and achieved satisfactory results. At the same time, AI plays an important role in the process of disease prediction and treatment evaluation for patients with IBD. However, we should also be aware that there are still some problems with AI. This paper gives a brief review of the practical application value of AI in IBD.

• Artificial intelligence
• Inflammatory bowel disease
• Ulcerative colitis
• Crohn's disease

Driven by the tremendous availability of data, artificial intelligence (AI) using deep learning has emerged as a breakthrough computer technology in the last few decades and has recently been acknowledged by the Task Force on AI as a golden opportunity for research. With its ability to understand, learn from and build on non-linear relationships, AI aims to individualize medical care in an attempt to save time, cost, effort and improve patient’s safety. AI has been applied in multiple medical fields with substantial progress made in gastroenterology mainly to facilitate accurate detection of pathology in different disease processes, among which inflammatory bowel disease (IBD) seems to drag significant attention, specifically by interpreting imaging studies, endoscopic images and videos and -to a lesser extent- disease genomics. Moreover, models have been built to predict IBD occurrence, flare ups, persistence of histological inflammation, disease-related structural abnormalities as well as disease remission. In this article, we will review the applications of AI in IBD in the present medical literature at multiple points of IBD timeline, starting from disease prediction via genomic assessment, diagnostic phase via interpretation of radiological studies and AI-assisted endoscopy, and the role of AI in the evaluation of therapy response and prognosis of IBD patients.

• Artificial intelligence
• Machine learning
• Inflammatory bowel disease

Inflammatory bowel disease (IBD) is a complex and multifaceted disorder of the gastrointestinal tract that is increasing in incidence worldwide and associated with significant morbidity. The rapid accumulation of large datasets from electronic health records, high-definition multi-omics (including genomics, proteomics, transcriptomics, and metagenomics), and imaging modalities (endoscopy and endomicroscopy) have provided powerful tools to unravel novel mechanistic insights and help address unmet clinical needs in IBD. Although the application of artificial intelligence (AI) methods has facilitated the analysis, integration, and interpretation of large datasets in IBD, significant heterogeneity in AI methods, datasets, and clinical outcomes and the need for unbiased prospective validations studies are current barriers to incorporation of AI into clinical practice. The purpose of this review is to summarize the most recent advances in the application of AI and machine learning technologies in the diagnosis and risk prediction, assessment of disease severity, and prediction of clinical outcomes in patients with IBD.

• Artificial intelligence
• Machine learning
• Inflammatory bowel disease
• Crohn’s disease
• Ulcerative colitis
• Clinical outcomes

• Artificial Intelligence
• Colitis
• Humans
• Inflammatory Bowel Diseases/diagnosis
• Inflammatory Bowel Diseases/therapy
• Metagenomics
• Prospective Studies

• Artificial intelligence
• BioMedia 2019 grand challenge
• Computer-aided detection and diagnosis
• Gastrointestinal endoscopy challenges
• Medical imaging
• Medico Task 2017
• Medico Task 2018

• Diagnostic Imaging
• Endoscopy
• Endoscopy, Gastrointestinal
• Humans

• Department of Health

• Autoimmune
• SSc skin
• deep learning
• mobilenet
• unet

• Benchmarking
• Deep learning
• Reproducibility of results
• Supervised machine learning

Inflammatory bowel disease (IBD) is a complex, immune-mediated gastrointestinal disorder with ill-defined etiology, multifaceted diagnostic criteria, and unpredictable treatment response. Innovations in IBD diagnostics, including developments in genomic sequencing and molecular analytics, have generated tremendous interest in leveraging these large data platforms into clinically meaningful tools. Artificial intelligence, through machine learning facilitates the interpretation of large arrays of data, and may provide insight to improving IBD outcomes. While potential applications of machine learning models are vast, further research is needed to generate standardized models that can be adapted to target IBD populations. 

• Artificial intelligence
• Machine learning
• Automated diagnostics
• Colorectal neoplasia screening
• Multiomic data
• Predictive models

• Artificial Intelligence
• Colitis
• Humans
• Inflammatory Bowel Diseases/diagnosis
• Inflammatory Bowel Diseases/therapy
• Machine Learning