A systematic review of artificial intelligence and machine learning applications to inflammatory bowel disease, with practical guidelines for interpretation

Abstract

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 individualised care. The use of ML methods for IBD was surveyed, with an additional focus on how the field has changed over time. Methods: 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”), searched 6th May 2021. Exclusion criteria: studies not written in English, no human patient data, publication before 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and record types that were not primary research. Results: 78 (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 datasets. The main applications of ML to clinical tasks were diagnosis (18/78), disease course (22/78) and disease severity (16/78). The median sample size was 263. Clinical and microbiome-related datasets were most popular. 5% of studies used an external dataset after training and testing for additional model validation.Discussion: Availability of longitudinal and deep phenotyping data could lead to better modelling. ML pipelines considering imbalanced data, and feature selection only on training data will generate more generalisable models. ML models are increasingly being applied to more complex clinical tasks for specific phenotypes, indicating progress towards personalised medicine for IBD