SynthesisFrontiers in pharmacology2020
Current Evidence on Computer-Aided Diagnosis of Celiac Disease: Systematic Review.
Synthesis in Frontiers in pharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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.
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Who cites it
15 citing papers in PubMed, 36 citations in OpenAlex.
- The needle study: Machine learning as a new method for case-finding in celiac disease.Journal of pediatric gastroenterology and nutrition · 2026Article
- Artificial Intelligence in Gastrointestinal Wireless Capsule Endoscopy: A Systematic Literature Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- Toward Precision Medicine in Celiac Disease: Emerging Roles of Multiomics and Artificial Intelligence.Analytical cellular pathology (Amsterdam) · 2025Review
- Cellular and molecular basis of proximal small intestine disorders.Nature reviews. Gastroenterology & hepatology · 2024Review
- Advancements in Computer-Aided Diagnosis of Celiac Disease: A Systematic Review.Biomimetics (Basel, Switzerland) · 2024Review
- Article
- Computer-Based Diagnosis of Celiac Disease by Quantitative Processing of Duodenal Endoscopy Images.Diagnostics (Basel, Switzerland) · 2023Article
- Convolutional neural network-based segmentation network applied to image recognition of angiodysplasias lesion under capsule endoscopy.World journal of gastroenterology · 2023Article
- Quantification of Celiac Disease Severity Using Video Capsule Endoscopy: A Comparison of Human Experts and Machine Learning Algorithms.Current medical imaging · 2023Article
- Recognizing the Emergent and Submerged Iceberg of the Celiac Disease: ITAMA Project-Global Strategy Protocol.Pediatric reports · 2022Article
- Artificial intelligence in small intestinal diseases: Application and prospects.World journal of gastroenterology · 2021Review
- Role of Artificial Intelligence in Video Capsule Endoscopy.Diagnostics (Basel, Switzerland) · 2021Review
- Precision medicine and machine learning towards the prediction of the outcome of potential celiac disease.Scientific reports · 2021Article
- Molecular Imaging of Inflammatory Disease.Biomedicines · 2021Review
- Celiac Disease as a Cause of Malabsorption: A Clinic-Pathological Series of Five Cases.Case reports in gastroenterologyArticle
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Celiac disease (CD) is a chronic autoimmune disease that occurs in genetically predisposed individuals in whom the ingestion of gluten leads to damage of the small bowel. It is estimated to affect 1 in 100 people worldwide, but is severely underdiagnosed. Currently available guidelines require CD-specific serology and atrophic histology in duodenal biopsy samples for the diagnosis of adult CD. In pediatric CD, but in recent years in adults also, nonbioptic diagnostic strategies have become increasingly popular. In this setting, in order to increase the diagnostic rate of this pathology, endoscopy itself has been thought of as a case finding strategy by use of digital image processing techniques. Research focused on computer aided decision support used as database video capsule, endoscopy and even biopsy duodenal images. Early automated methods for diagnosis of celiac disease used feature extraction methods like spatial domain features, transform domain features, scale-invariant features and spatio-temporal features. Recent artificial intelligence (AI) techniques using deep learning (DL) methods such as convolutional neural network (CNN), support vector machines (SVM) or Bayesian inference have emerged as a breakthrough computer technology which can be used for computer aided diagnosis of celiac disease. In the current review we summarize methods used in clinical studies for classification of CD from feature extraction methods to AI techniques.
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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 OpenQuestion graph.