SynthesisInflammatory bowel diseases2022
A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation.
Synthesis in Inflammatory bowel diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
37 citing papers in PubMed, 49 citations in OpenAlex.
- Molecular Imaging in Pancreatic Cancer: Current Applications and Future Perspectives.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Decoding Microbiota-Immune Interplay in Viral Pathogenesis: Toward Next-Generation Antiviral Therapies.Probiotics and antimicrobial proteins · 2026Review
- An Attention-Enhanced ViT-HLNN Hybrid Ensemble Framework for Multi-Class Gastrointestinal Disease Classification.Scientific reports · 2026Article
- Multimodal deep learning for inflammatory bowel disease: a new frontier in cellular and molecular biomarker discovery to clinical translation.Journal of biological engineering · 2026Review
- MicroRNA‑microbiome cross‑kingdom networks drive inflammatory bowel disease through dynamic regulatory ecosystems (Review).International journal of molecular medicine · 2026Review
- Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges.World journal of hepatology · 2026Review
- Artificial intelligence in inflammatory bowel disease: Current applications and future directions.World journal of gastroenterology · 2025Review
- Gastroenterology in the age of artificial intelligence: Bridging technology and clinical practice.World journal of gastroenterology · 2025Review
- Explainable artificial intelligence for personalized management of inflammatory bowel disease: A minireview of recent advances.World journal of gastroenterology · 2025Review
- IgG4 unveiled: navigating the interplay with Crohn's disease - from immunology insights to machine learning.Annals of medicine and surgery (2012) · 2025Review
- Artificial Intelligence in Endoscopic and Ultrasound Imaging for Inflammatory Bowel Disease.Journal of clinical medicine · 2025Review
- Diagnostic Methods and Biomarkers in Inflammatory Bowel Disease.Diagnostics (Basel, Switzerland) · 2025Review
- Utility of Machine Learning to Characterize Gut Microbiota Dysbiosis and Its Clinical Implications in Inflammatory Bowel Disease.Journal of inflammation research · 2025Review
- Artificial intelligence to revolutionize IBD clinical trials: a comprehensive review.Therapeutic advances in gastroenterology · 2025Review
- Automated inflammatory bowel disease detection using wearable bowel sound event spotting.Frontiers in digital health · 2025Article
- Detection of protein-losing enteropathy (PLE) ultrasonographic imaging features in dogs using deep learning neural networks.Frontiers in artificial intelligence · 2025Article
- Machine learning in the differential diagnosis of ulcerative colitis and Crohn's disease: a systematic review.Translational gastroenterology and hepatology · 2025Review
- Identifying inflammatory bowel disease subtypes: a comprehensive exploration of transcriptomic data and machine learning-based approaches.Therapeutic advances in gastroenterology · 2025Article
- Artificial intelligence-based multimodal model for the identification of ulcerative colitis with concomitant cytomegalovirus colitis.Therapeutic advances in gastroenterology · 2025Article
- Effectiveness of Telemedicine in Inflammatory Bowel Disease in Russia: TIGE-Rus (Telemonitoring for IBD Goodness Examination in Russia) Study Protocol of a Randomized Controlled Trial.Journal of clinical medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundInflammatory 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.
methodsOn 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.
resultsSeventy-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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.