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Review
. 2024 Sep 5:17:17562848241272001.
doi: 10.1177/17562848241272001. eCollection 2024.

Artificial intelligence and machine learning technologies in ulcerative colitis

Chiraag Kulkarni  1 , Derek Liu  1 , Touran Fardeen  1 , Eliza Rose Dickson  1 , Hyunsu Jang  1 , Sidhartha R Sinha  2 , John Gubatan  2
Affiliations
Review

Artificial intelligence and machine learning technologies in ulcerative colitis

Chiraag Kulkarni et al. Therap Adv Gastroenterol. .

Abstract

Interest in artificial intelligence (AI) applications for ulcerative colitis (UC) has grown tremendously in recent years. In the past 5 years, there have been over 80 studies focused on machine learning (ML) tools to address a wide range of clinical problems in UC, including diagnosis, prognosis, identification of new UC biomarkers, monitoring of disease activity, and prediction of complications. AI classifiers such as random forest, support vector machines, neural networks, and logistic regression models have been used to model UC clinical outcomes using molecular (transcriptomic) and clinical (electronic health record and laboratory) datasets with relatively high performance (accuracy, sensitivity, and specificity). Application of ML algorithms such as computer vision, guided image filtering, and convolutional neural networks have also been utilized to analyze large and high-dimensional imaging datasets such as endoscopic, histologic, and radiological images for UC diagnosis and prediction of complications (post-surgical complications, colorectal cancer). Incorporation of these ML tools to guide and optimize UC clinical practice is promising but will require large, high-quality validation studies that overcome the risk of bias as well as consider cost-effectiveness compared to standard of care.

Keywords: artificial intelligence; biomarkers; machine learning; outcomes; prediction; ulcerative colitis.

Plain language summary

Artificial intelligence in ulcerative colitis Ulcerative colitis (UC) is a chronic inflammatory disorder of the colon. The clinical care of patients with UC and research efforts to better understand the disease has inevitably produced a significant quantity of diverse and complex datasets ranging from electronic health records, laboratory values, images (endoscopy, radiology, histology) to gene expression. The size and complexity of datasets derived from UC poses a significant challenge to accurately and effectively predict clinically meaningful endpoints in order to ultimately improve UC outcomes. Artificial intelligence through the application of machine learning tools has the potential to improve the analysis of large, complex, high-dimensional datasets and reveal novel, deeper insights compared to traditional analytical tools. Here, we provide an updated and comprehensive summary of AI applications in UC.

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Conflict of interest statement

The authors declare that there is no conflict of interest.

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