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

2021 ◽  
Vol 36 (2) ◽  
pp. 279-285
Author(s):  
Shirley Cohen‐Mekelburg ◽  
Sameer Berry ◽  
Ryan W Stidham ◽  
Ji Zhu ◽  
Akbar K Waljee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Clinical Applications ◽  
Inflammatory Bowel

scholarly journals Machine Learning Modeling from Omics Data as Prospective Tool for Improvement of Inflammatory Bowel Disease Diagnosis and Clinical Classifications

Genes ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1438
Author(s):  
Biljana Stankovic ◽  
Nikola Kotur ◽  
Gordana Nikcevic ◽  
Vladimir Gasic ◽  
Branka Zukic ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Disease Diagnosis ◽  
Omics Data ◽  
Current Application ◽  
Inflammatory Bowel ◽  
Future Outcomes ◽  
Clinical Records

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.

scholarly journals Emerging use of artificial intelligence in inflammatory bowel disease

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

Artificial intelligence for endoscopy in inflammatory bowel disease

2022 ◽  
Author(s):  
Kento Takenaka ◽  
Ami Kawamoto ◽  
Ryuichi Okamoto ◽  
Mamoru Watanabe ◽  
Kazuo Ohtsuka
Keyword(s):  
Artificial Intelligence ◽  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Inflammatory Bowel

scholarly journals 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

2020 ◽  
Vol 9 (11) ◽  
pp. 3427 ◽  
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.

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

F1000Research ◽  
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.

scholarly journals Immunological Profiling of Paediatric Inflammatory Bowel Disease Using Unsupervised Machine Learning

2020 ◽  
Vol 70 (6) ◽  
pp. 833-840
Author(s):  
Tracy Coelho ◽  
Enrico Mossotto ◽  
Yifang Gao ◽  
Rachel Haggarty ◽  
James J. Ashton ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Unsupervised Machine Learning ◽  
Inflammatory Bowel

scholarly journals Classification of Paediatric Inflammatory Bowel Disease using Machine Learning

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
E. Mossotto ◽  
J. J. Ashton ◽  
T. Coelho ◽  
R. M. Beattie ◽  
B. D. MacArthur ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Inflammatory Bowel
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