ArticleCurrent medical imaging2023
Quantification of Celiac Disease Severity Using Video Capsule Endoscopy: A Comparison of Human Experts and Machine Learning Algorithms.
Article in Current medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Artificial Intelligence-Assisted Capsule Endoscopy Versus Conventional Capsule Endoscopy for Detection of Small Bowel Lesions: A Systematic Review and Meta-Analysis.Journal of gastroenterology and hepatology · 2025Pooled it
- The needle study: Machine learning as a new method for case-finding in celiac disease.Journal of pediatric gastroenterology and nutrition · 2026Article
- Artificial intelligence-augmented small bowel capsule endoscopy for coeliac disease: a literature review on accuracy, workflow, and safety.Translational gastroenterology and hepatology · 2026Review
- Gluten identification from food images using advanced deep learning and transfer learning methods.Journal of food science and technology · 2025Article
- Toward Precision Medicine in Celiac Disease: Emerging Roles of Multiomics and Artificial Intelligence.Analytical cellular pathology (Amsterdam) · 2025Review
- The utility of artificial intelligence in visualization of pediatric gastrointestinal mucosa.Frontiers in pediatrics · 2025Review
- Celiac Disease Deep Learning Image Classification Using Convolutional Neural Networks.Journal of imaging · 2024Article
- Advancements in Computer-Aided Diagnosis of Celiac Disease: A Systematic Review.Biomimetics (Basel, Switzerland) · 2024Review
- Past, Present, and Future of Noninvasive Tests to Assess Gluten Exposure, Celiac Disease Activity, and End-Organ Damage.Gastroenterology · 2024Review
- From Data to Insights: How Is AI Revolutionizing Small-Bowel Endoscopy?Diagnostics (Basel, Switzerland) · 2024Review
- Deep Learning in Coeliac Disease: A Systematic Review on Novel Diagnostic Approaches to Disease Diagnosis.Journal of clinical medicine · 2023Review
- Editorial: Celebrating women researchers and a focus on non-invasive modalities and innovation.Therapeutic advances in gastrointestinal endoscopyArticle
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundVideo capsule endoscopy (VCE) is an attractive method for diagnosing and objectively monitoring disease activity in celiac disease (CeD). Its use, facilitated by artificial intelligence- based tools, may allow computer-assisted interpretation of VCE studies, transforming a subjective test into a quantitative and reproducible measurement tool.
objectiveTo evaluate and compare objective CeD severity assessment as determined with VCE by expert human readers and a machine learning algorithm (MLA).
methodsPatients ≥ 18 years with histologically proven CeD underwent VCE. Examination frames were scored by three readers from one center and the MLA, using a 4-point ordinal scale for assessing the severity of CeD enteropathy. After scoring, curves representing CeD severity across the entire small intestine (SI) and individual tertiles (proximal, mid, and distal) were fitted for each reader and the MLA. All comparisons used Krippendorff's alpha; values > 0.8 represent excellent to 'almost perfect' inter-reader agreement.
resultsVCEs from 63 patients were scored. Readers demonstrated strong inter-reader agreement on celiac villous damage (alpha=0.924), and mean value reader curves showed similarly excellent agreement with MLA curves (alpha=0.935). Average reader and MLA curves were comparable for mean and maximum values for the first SI tertile (alphas=0.932 and 0.867, respectively) and the mean value over the entire SI (alpha=0.945).
conclusionA novel MLA demonstrated excellent agreement on whole SI imaging with three expert gastroenterologists. An ordinal scale permitted high inter-reader agreement, accurately and reliably replicated by the MLA. Interpreting VCEs using MLAs may allow automated diagnosis and disease burden assessment in CeD.
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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.