A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation

Inflamm Bowel Dis. 2022 Jun 14;izac115. doi: 10.1093/ibd/izac115. Online ahead of print.


Imogen S Stafford 1 2 3Mark M Gosink 4Enrico Mossotto 1Sarah Ennis 1Manfred Hauben 4 5


Author information

1Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK.

2Institute for Life Sciences, University Of Southampton, Southampton, UK.

3NIHR Southampton Biomedical Research, University Hospital Southampton, Southampton, UK.

4Pfizer Inc, New York, NY, USA.

5NYU Langone Health, Department of Medicine, New York, NY, USA.


Background: Inflammatory bowel disease (IBD) is a gastrointestinal chronic disease with an unpredictable disease course. Computational methods such as machine learning (ML) have the potential to stratify IBD patients for the provision of individualized care. The use of ML methods for IBD was surveyed, with an additional focus on how the field has changed over time.

Methods: On May 6, 2021, a systematic review was conducted through a search of MEDLINE and Embase databases, with the search structure ("machine learning" OR "artificial intelligence") AND ("Crohn* Disease" OR "Ulcerative Colitis" OR "Inflammatory Bowel Disease"). Exclusion criteria included studies not written in English, no human patient data, publication before 2001, studies that were not peer reviewed, nonautoimmune disease comorbidity research, and record types that were not primary research.

Results: Seventy-eight (of 409) records met the inclusion criteria. Random forest methods were most prevalent, and there was an increase in neural networks, mainly applied to imaging data sets. The main applications of ML to clinical tasks were diagnosis (18 of 78), disease course (22 of 78), and disease severity (16 of 78). The median sample size was 263. Clinical and microbiome-related data sets were most popular. Five percent of studies used an external data set after training and testing for additional model validation.

Discussion: Availability of longitudinal and deep phenotyping data could lead to better modeling. Machine learning pipelines that consider imbalanced data and that feature selection only on training data will generate more generalizable models. Machine learning models are increasingly being applied to more complex clinical tasks for specific phenotypes, indicating progress towards personalized medicine for IBD.



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