ArticleScientific reports2019
Automatic classification of IgA endomysial antibody test for celiac disease: a new method deploying machine learning.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 24 citations in OpenAlex.
- Machine learning-based prediction of 1-year mortality risk after off-pump coronary artery bypass grafting.Journal of thoracic disease · 2026Article
- Low positive and borderline negative transglutaminase antibody levels are frequently associated with a coeliac disease diagnosis.Journal of internal medicine · 2025Article
- Recent advances in the diagnosis of celiac disease.World journal of gastroenterology · 2025Article
- Biopsy-Sparing Diagnosis of Coeliac Disease Based on Endomysial Antibody Testing and Clinical Risk Assessment.Alimentary pharmacology & therapeutics · 2025Article
- Article
- Toward Precision Medicine in Celiac Disease: Emerging Roles of Multiomics and Artificial Intelligence.Analytical cellular pathology (Amsterdam) · 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
- A user-friendly machine learning approach for cardiac structures assessment.Frontiers in cardiovascular medicine · 2024Article
- Evaluating the Efficacy of ChatGPT in Navigating the Spanish Medical Residency Entrance Examination (MIR): Promising Horizons for AI in Clinical Medicine.Clinics and practice · 2023Article
- Using machine learning to aid treatment decision and risk assessment for severe three-vessel coronary artery disease.Journal of geriatric cardiology : JGC · 2022Article
- Clinical Applications of Artificial Intelligence-An Updated Overview.Journal of clinical medicine · 2022Review
- Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology.World journal of gastroenterology · 2021Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Widespread use of endomysial autoantibody (EmA) test in diagnostics of celiac disease is limited due to its subjectivity and its requirement of an expert evaluator. The study aimed to determine whether machine learning can be applied to create a new observer-independent method of automatic assessment and classification of the EmA test for celiac disease. The study material comprised of 2597 high-quality IgA-class EmA images collected in 2017-2018. According to standard procedure, highly-experienced professional classified samples into the following four classes: I - positive, II - negative, III - IgA deficient, and IV - equivocal. Machine learning was deployed to create a classification model. The sensitivity and specificity of the model were 82.84% and 99.40%, respectively. The accuracy was 96.80%. The classification error was 3.20%. The area under the curve was 99.67%, 99.61%, 100%, and 99.89%, for I, II, III, and IV class, respectively. The mean assessment time per image was 16.11 seconds. This is the first study deploying machine learning for the automatic classification of IgA-class EmA test for celiac disease. The results indicate that using machine learning enables quick and precise EmA test analysis that can be further developed to simplify EmA analysis.
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