Clinical applications of artificial intelligence and machine learning‐based methods in inflammatory bowel disease

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.

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Emerging use of artificial intelligence in inflammatory bowel disease

World Journal of Gastroenterology ◽

10.3748/wjg.v26.i44.6923 ◽

2020 ◽

Vol 26 (44) ◽

pp. 6923-6928

Author(s):

Arushi Kohli ◽

Erik A Holzwanger ◽

Alexander N Levy

Keyword(s):

Artificial Intelligence ◽

Inflammatory Bowel Disease ◽

Bowel Disease ◽

Inflammatory Bowel

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Artificial intelligence for endoscopy in inflammatory bowel disease

Intestinal Research ◽

10.5217/ir.2021.00079 ◽

2022 ◽

Author(s):

Kento Takenaka ◽

Ami Kawamoto ◽

Ryuichi Okamoto ◽

Mamoru Watanabe ◽

Kazuo Ohtsuka

Keyword(s):

Artificial Intelligence ◽

Inflammatory Bowel Disease ◽

Bowel Disease ◽

Inflammatory Bowel

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Development of Machine Learning Model to Predict the 5-Year Risk of Starting Biologic Agents in Patients with Inflammatory Bowel Disease (IBD): K-CDM Network Study

Journal of Clinical Medicine ◽

10.3390/jcm9113427 ◽

2020 ◽

Vol 9 (11) ◽

pp. 3427 ◽

Cited By ~ 1

Author(s):

Youn I Choi ◽

Sung Jin Park ◽

Jun-Won Chung ◽

Kyoung Oh Kim ◽

Jae Hee Cho ◽

...

Keyword(s):

Machine Learning ◽

Inflammatory Bowel Disease ◽

Validation Study ◽

Bowel Disease ◽

Medical Center ◽

External Validation ◽

Biologic Agents ◽

Risk Level ◽

Common Data Model ◽

Inflammatory Bowel

Background: The incidence and global burden of inflammatory bowel disease (IBD) have steadily increased in the past few decades. Improved methods to stratify risk and predict disease-related outcomes are required for IBD. Aim: The aim of this study was to develop and validate a machine learning (ML) model to predict the 5-year risk of starting biologic agents in IBD patients. Method: We applied an ML method to the database of the Korean common data model (K-CDM) network, a data sharing consortium of tertiary centers in Korea, to develop a model to predict the 5-year risk of starting biologic agents in IBD patients. The records analyzed were those of patients diagnosed with IBD between January 2006 and June 2017 at Gil Medical Center (GMC; n = 1299) or present in the K-CDM network (n = 3286). The ML algorithm was developed to predict 5- year risk of starting biologic agents in IBD patients using data from GMC and externally validated with the K-CDM network database. Result: The ML model for prediction of IBD-related outcomes at 5 years after diagnosis yielded an area under the curve (AUC) of 0.86 (95% CI: 0.82–0.92), in an internal validation study carried out at GMC. The model performed consistently across a range of other datasets, including that of the K-CDM network (AUC = 0.81; 95% CI: 0.80–0.85), in an external validation study. Conclusion: The ML-based prediction model can be used to identify IBD-related outcomes in patients at risk, enabling physicians to perform close follow-up based on the patient’s risk level, estimated through the ML algorithm.

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Clinical Applications of CAM for Inflammatory Bowel Disease

Alternative and Complementary Therapies ◽

10.1089/act.2007.13308 ◽

2007 ◽

Vol 13 (3) ◽

pp. 121-124

Author(s):

Jane Hart

Keyword(s):

Inflammatory Bowel Disease ◽

Bowel Disease ◽

Clinical Applications ◽

Inflammatory Bowel

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Machine Learning–Based Gene Prioritization Identifies Novel Candidate Risk Genes for Inflammatory Bowel Disease

Inflammatory Bowel Diseases ◽

10.1097/mib.0000000000001222 ◽

2017 ◽

Vol 23 (9) ◽

pp. 1516-1523 ◽

Cited By ~ 10

Author(s):

Ofer Isakov ◽

Iris Dotan ◽

Shay Ben-Shachar

Keyword(s):

Machine Learning ◽

Inflammatory Bowel Disease ◽

Bowel Disease ◽

Gene Prioritization ◽

Risk Genes ◽

Inflammatory Bowel

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Investigation of gut microbiome association with inflammatory bowel disease and depression: a machine learning approach

F1000Research ◽

10.12688/f1000research.15091.2 ◽

2019 ◽

Vol 7 ◽

pp. 702

Author(s):

Pedro Morell Miranda ◽

Francesca Bertolini ◽

Haja N. Kadarmideen

Keyword(s):

Machine Learning ◽

Inflammatory Bowel Disease ◽

Random Forest ◽

Bowel Disease ◽

Gut Microbiome ◽

Predictive Power ◽

Learning Approach ◽

Important Species ◽

Machine Learning Approach ◽

Inflammatory Bowel

Background: Inflammatory bowel disease (IBD) is a group of chronic diseases related to inflammatory processes in the digestive tract generally associated with an immune response to an altered gut microbiome in genetically predisposed subjects. For years, both researchers and clinicians have been reporting increased rates of anxiety and depression disorders in IBD, and these disorders have also been linked to an altered microbiome. However, the underlying pathophysiological mechanisms of comorbidity are poorly understood at the gut microbiome level. Methods: Metagenomic and metatranscriptomic data were retrieved from the Inflammatory Bowel Disease Multi-Omics Database. Samples from 70 individuals that had answered to a self-reported depression and anxiety questionnaire were selected and classified by their IBD diagnosis and their questionnaire results, creating six different groups. The cross-validation random forest algorithm was used in 90% of the individuals (training set) to retain the most important species involved in discriminating the samples without losing predictive power. The validation set that represented the remaining 10% of the samples equally distributed across the six groups was used to train a random forest using only the species selected in order to evaluate their predictive power. Results: A total of 24 species were identified as the most informative in discriminating the 6 groups. Several of these species were frequently described in dysbiosis cases, such as species from the genus Bacteroides and Faecalibacterium prausnitzii. Despite the different compositions among the groups, no common patterns were found between samples classified as depressed. However, distinct taxonomic profiles within patients of IBD depending on their depression status were detected. Conclusions: The machine learning approach is a promising approach for investigating the role of microbiome in IBD and depression. Abundance and functional changes in these species suggest that depression should be considered as a factor in future research on IBD.

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