ArticleScientific reports2022
An artificial intelligence algorithm is highly accurate for detecting endoscopic features of eosinophilic esophagitis.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Esophageal and Oropharyngeal Dysphagia: Clinical Recommendations From the United European Gastroenterology and European Society for Neurogastroenterology and Motility.United European gastroenterology journal · 2025Guideline
- Advancing the understanding of eosinophilic esophagitis: From pathogenesis to novel therapies.World journal of gastrointestinal pathophysiology · 2026Review
- Endoscopic Diagnosis of Eosinophilic Esophagitis Using a Multi-Task U-Net: A Pilot Study.Yonsei medical journal · 2026Article
- Can artificial intelligence improve the diagnosis and management of patients with eosinophilic esophagitis?World journal of gastroenterology · 2025Review
- Artificial Intelligence in Upper Gastrointestinal Diagnosis.The Korean journal of helicobacter and upper gastrointestinal research · 2025Review
- Endoscopic Management of Eosinophilic Esophagitis: A Narrative Review on Diagnosis and Treatment.Journal of clinical medicine · 2025Review
- Advancing artificial intelligence applicability in endoscopy through source-agnostic camera signal extraction from endoscopic images.PloS one · 2025Article
- Application of artificial intelligence in eosinophilic esophagitis.Frontiers in immunology · 2025Review
- Machine learning-based identification and characterization of mast cells in eosinophilic esophagitis.The Journal of allergy and clinical immunology · 2024Article
- The Dual Lens of Endoscopy and Histology in the Diagnosis and Management of Eosinophilic Gastrointestinal Disorders-A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2024Review
- Artificial intelligence-based polyp size measurement in gastrointestinal endoscopy using the auxiliary waterjet as a reference.Endoscopy · 2023Article
- Genetic and Molecular Contributors in Eosinophilic Esophagitis.Current allergy and asthma reports · 2023Review
- Endoscopic Diagnosis of Eosinophilic Esophagitis: Basics and Recent Advances.Diagnostics (Basel, Switzerland) · 2022Review
- Artificial intelligence in gastrointestinal endoscopy: current evidence and future directions.Therapeutic advances in gastrointestinal endoscopyReview
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Authors and funding
20 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The endoscopic features associated with eosinophilic esophagitis (EoE) may be missed during routine endoscopy. We aimed to develop and evaluate an Artificial Intelligence (AI) algorithm for detecting and quantifying the endoscopic features of EoE in white light images, supplemented by the EoE Endoscopic Reference Score (EREFS). An AI algorithm (AI-EoE) was constructed and trained to differentiate between EoE and normal esophagus using endoscopic white light images extracted from the database of the University Hospital Augsburg. In addition to binary classification, a second algorithm was trained with specific auxiliary branches for each EREFS feature (AI-EoE-EREFS). The AI algorithms were evaluated on an external data set from the University of North Carolina, Chapel Hill (UNC), and compared with the performance of human endoscopists with varying levels of experience. The overall sensitivity, specificity, and accuracy of AI-EoE were 0.93 for all measures, while the AUC was 0.986. With additional auxiliary branches for the EREFS categories, the AI algorithm (AI-EoE-EREFS) performance improved to 0.96, 0.94, 0.95, and 0.992 for sensitivity, specificity, accuracy, and AUC, respectively. AI-EoE and AI-EoE-EREFS performed significantly better than endoscopy beginners and senior fellows on the same set of images. An AI algorithm can be trained to detect and quantify endoscopic features of EoE with excellent performance scores. The addition of the EREFS criteria improved the performance of the AI algorithm, which performed significantly better than endoscopists with a lower or medium experience level.
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