ArticleJournal of pathology informatics2019
Automated Detection of Celiac Disease on Duodenal Biopsy Slides: A Deep Learning Approach.
Article in Journal of pathology informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it, 89 citations in OpenAlex.
- Diagnostic accuracy of deep learning in detection and prognostication of renal cell carcinoma: a systematic review and meta-analysis.Frontiers in medicine · 2024Pooled it
- Generalisability of an adult-trained AI model to paediatric coeliac disease diagnosis: a multicentre external validation study.EClinicalMedicine · 2026Article
- Artificial Intelligence for Tumor Tissue Detection in Stomach Cancer: A Retrospective Algorithm Development and Validation Study.Journal of clinical medicine · 2026Article
- Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records.The American journal of pathology · 2026Article
- The Rise of AI-Assisted Diagnosis: Will Pathologists Be Partners or Bystanders?Diagnostics (Basel, Switzerland) · 2025Review
- Automated Segmentation and Morphometric Analysis of Thioflavin-S-Stained Amyloid Deposits in Alzheimer's Disease Brains and Age-Matched Controls Using Weakly Supervised Deep Learning.International journal of molecular sciences · 2025Article
- Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice.PLOS digital health · 2025Article
- Deep Learning for Classification of Inflammatory Bowel Disease Activity in Whole Slide Images of Colonic Histopathology.The American journal of pathology · 2025Article
- Article
- Toward Precision Medicine in Celiac Disease: Emerging Roles of Multiomics and Artificial Intelligence.Analytical cellular pathology (Amsterdam) · 2025Review
- Role of Artificial Intelligence in Nanomedicine and Organ-specific Therapy: An Updated Review.Current drug targets · 2025Review
- Article
- Interpretable machine learning-based detection of coeliac disease.BMJ digital health & AI · 2025Article
- A feasibility study using quantitative and interpretable histological analyses of celiac disease for automated cell type and tissue area classification.Scientific reports · 2024Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Celiac Disease Deep Learning Image Classification Using Convolutional Neural Networks.Journal of imaging · 2024Article
- Application of automated machine learning for histological evaluation of feline endoscopic samples.The Journal of veterinary medical science · 2024Article
- CD, or not CD, that is the question: a digital interobserver agreement study in coeliac disease.BMJ open gastroenterology · 2024Article
- Pathologist-trained machine learning classifiers developed to quantitate celiac disease features differentiate endoscopic biopsies according to modified marsh score and dietary intervention response.Diagnostic pathology · 2023Article
- Detection of Colorectal Adenocarcinoma and Grading Dysplasia on Histopathologic Slides Using Deep Learning.The American journal of pathology · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
contextCeliac disease (CD) prevalence and diagnosis have increased substantially in recent years. The current gold standard for CD confirmation is visual examination of duodenal mucosal biopsies. An accurate computer-aided biopsy analysis system using deep learning can help pathologists diagnose CD more efficiently. SUBJECTS AND
methodsIn this study, we trained a deep learning model to detect CD on duodenal biopsy images. Our model uses a state-of-the-art residual convolutional neural network to evaluate patches of duodenal tissue and then aggregates those predictions for whole-slide classification. We tested the model on an independent set of 212 images and evaluated its classification results against reference standards established by pathologists.
resultsOur model identified CD, normal tissue, and nonspecific duodenitis with accuracies of 95.3%, 91.0%, and 89.2%, respectively. The area under the receiver operating characteristic curve was >0.95 for all classes.
conclusionsWe have developed an automated biopsy analysis system that achieves high performance in detecting CD on biopsy slides. Our system can highlight areas of interest and provide preliminary classification of duodenal biopsies before review by pathologists. This technology has great potential for improving the accuracy and efficiency of CD diagnosis.
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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.