Emerging use of artificial intelligence in inflammatory bowel disease

Kohli, Arushi; Holzwanger, Erik; Levy, Alexander N.

doi:10.3748/wjg.v26.i44.6923

Cited by 23 publications

(20 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning (ML) [ 204 ] is a subdomain of AI that deals with the development of algorithms that learns to perform a task through experience (i.e., data) without being explicitly programmed to do so. Recent developments in AI and ML research allowed their applications both in surgery and gastroenterology [ 205 , 206 , 207 , 208 , 209 ]. These innovative approaches could represent a step forward in the IBD diagnosis and management, especially for the capability to analyze and make predictions on wide, unstructured datasets.…”

Section: Future Perspectives On Ibd Managementmentioning

confidence: 99%

Challenges in Crohn’s Disease Management after Gastrointestinal Cancer Diagnosis

Fiorillo

Schena

Quero

et al. 2021

Cancers

View full text Add to dashboard Cite

Crohn’s disease (CD) is a chronic inflammatory bowel disease with a progressive course, potentially affecting the entire gastrointestinal tract from mouth to anus. Several studies have shown an increased risk of both intestinal and extra-intestinal cancer in patients with CD, due to long-standing transmural inflammation and damage accumulation. The similarity of symptoms among CD, its related complications and the de novo onset of gastrointestinal cancer raises difficulties in the differential diagnosis. In addition, once a cancer diagnosis in CD patients is made, selecting the appropriate treatment can be particularly challenging. Indeed, both surgical and oncological treatments are not always the same as that of the general population, due to the inflammatory context of the gastrointestinal tract and the potential exacerbation of gastrointestinal symptoms of patients with CD; moreover, the overlap of the neoplastic disease could lead to adjustments in the pharmacological treatment of the underlying CD, especially with regard to immunosuppressive drugs. For these reasons, a case-by-case analysis in a multidisciplinary approach is often appropriate for the best diagnostic and therapeutic evaluation of patients with CD after gastrointestinal cancer onset.

show abstract

Section: Future Perspectives On Ibd Managementmentioning

confidence: 99%

Challenges in Crohn’s Disease Management after Gastrointestinal Cancer Diagnosis

Fiorillo

Schena

Quero

et al. 2021

Cancers

View full text Add to dashboard Cite

show abstract

“…Despite some limitations, there is much interest in developing and testing machine learning and deep learning tools to aid decision making[ 5 , 7 ]. In luminal gastroenterology, machine learning is gaining traction but its use has been relatively limited to automatic image recognition in endoscopy[ 8 - 11 ] as well as feature selection in genomic and microbiomics data[ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Deep learning vs conventional learning algorithms for clinical prediction in Crohn's disease: A proof-of-concept study

Con¹,

Langenberg²,

Vasudevan³

2021

WJG

View full text Add to dashboard Cite

BACKGROUND Traditional methods of developing predictive models in inflammatory bowel diseases (IBD) rely on using statistical regression approaches to deriving clinical scores such as the Crohn's disease (CD) activity index. However, traditional approaches are unable to take advantage of more complex data structures such as repeated measurements. Deep learning methods have the potential ability to automatically find and learn complex, hidden relationships between predictive markers and outcomes, but their application to clinical prediction in CD and IBD has not been explored previously. AIM To determine and compare the utility of deep learning with conventional algorithms in predicting response to anti-tumor necrosis factor (anti-TNF) therapy in CD. METHODS This was a retrospective single-center cohort study of all CD patients who commenced anti-TNF therapy (either adalimumab or infliximab) from January 1, 2010 to December 31, 2015. Remission was defined as a C-reactive protein (CRP) < 5 mg/L at 12 mo after anti-TNF commencement. Three supervised learning algorithms were compared: (1) A conventional statistical learning algorithm using multivariable logistic regression on baseline data only; (2) A deep learning algorithm using a feed-forward artificial neural network on baseline data only; and (3) A deep learning algorithm using a recurrent neural network on repeated data. Predictive performance was assessed using area under the receiver operator characteristic curve (AUC) after 10× repeated 5-fold cross-validation. RESULTS A total of 146 patients were included (median age 36 years, 48% male). Concomitant therapy at anti-TNF commencement included thiopurines (68%), methotrexate (18%), corticosteroids (44%) and aminosalicylates (33%). After 12 mo, 64% had CRP < 5 mg/L. The conventional learning algorithm selected the following baseline variables for the predictive model: Complex disease behavior, albumin, monocytes, lymphocytes, mean corpuscular hemoglobin concentration and gamma-glutamyl transferase, and had a cross-validated AUC of 0.659, 95% confidence interval (CI): 0.562-0.756. A feed-forward artificial neural network using only baseline data demonstrated an AUC of 0.710 (95%CI: 0.622-0.799; P = 0.25 vs conventional). A recurrent neural network using repeated biomarker measurements demonstrated significantly higher AUC compared to the conventional algorithm (0.754, 95%CI: 0.674-0.834; P = 0.036). CONCLUSION Deep learning methods are feasible and have the potential for stronger predictive performance compared to conventional model building methods when applied to predicting remission after anti-TNF therapy in CD.

show abstract

“…Currently, there is no cure for IBD, and in a significant number of cases, applied therapies are found to be ineffective or lead to a poor/inadequate response [ 25 , 26 , 27 ]. In addition, it is often not possible to establish an accurate diagnosis of IBD since it depends on a combination of numerous clinical data, including complex image assessments, whose interpretation is inherently subjective [ 28 ]. Altogether, untimely and inaccurate diagnosis has a great impact on the course of the disease, which usually leads to complications and thus represents a serious obstacle to achieving and maintaining remission of the disease, which is the main goal of the IBD treatment [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

“…The analysis of omics big data demands the usage of powerful bioinformatics tools and application of advanced statistics such as artificial intelligence (AI). Machine learning (ML), a subset of AI, is the most promising tool nowadays in search of new clinically relevant patterns and reliable predictive markers of complex diseases [ 28 , 37 ]. The underlying genetic predisposition to IBD has not been completely revealed employing only the candidate gene approach or genome wide association studies (GWAS).…”

Section: Introductionmentioning

confidence: 99%

“…The underlying genetic predisposition to IBD has not been completely revealed employing only the candidate gene approach or genome wide association studies (GWAS). For that reason, there is great interest in the application of AI in IBD research with the goal to improve: patient identification, differential diagnosis, disease risk prediction and clinical outcomes and classification of disease subtypes, as well as identification of disease biomarkers that could be targeted for advancing therapeutic management ( Figure 1 ) [ 1 , 28 , 38 , 39 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Stanković

Kotur

Nikčević

et al. 2021

Genes

View full text Add to dashboard Cite

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.

show abstract

Emerging use of artificial intelligence in inflammatory bowel disease

Cited by 23 publications

References 34 publications

Challenges in Crohn’s Disease Management after Gastrointestinal Cancer Diagnosis

Challenges in Crohn’s Disease Management after Gastrointestinal Cancer Diagnosis

Deep learning vs conventional learning algorithms for clinical prediction in Crohn's disease: A proof-of-concept study

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

Contact Info

Product

Resources

About