ArticleFrontiers in pediatrics2023
Pediatric Crohn's disease diagnosis aid
Article in Frontiers in pediatrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Advances in non-invasive biomarkers for pediatric inflammatory bowel disease diagnosis.World journal of gastroenterology · 2026Article
- Pharmacogenomic Pathways Underlying Variable Vedolizumab Response in Crohn's Disease Patients: A Rare-Variant Analysis.Biomedicines · 2026Article
- Artificial intelligence use for precision medicine in inflammatory bowel disease: a systematic review.American journal of translational research · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Determination of pediatric Crohn's disease (CD) remains a major diagnostic challenge. However, the rapidly emerging field of artificial intelligence has demonstrated promise in developing diagnostic models for intractable diseases. Methods: We propose an artificial neural network model of 8 gene markers identified by 4 classification algorithms based on Gene Expression Omnibus database for diagnostic of pediatric CD. Results: The model achieved over 85% accuracy and area under ROC curve value in both training set and testing set for diagnosing pediatric CD. Additionally, immune infiltration analysis was performed to address why these markers can be integrated to develop a diagnostic model. Conclusion: This study supports further clinical facilitation of precise disease diagnosis by integrating genomics and machine learning algorithms in open-access database.
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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.