ArticleInternational journal of environmental research and public health2021
A Deep Learning Model for Classification of Endoscopic Gastroesophageal Reflux Disease.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Towards a neuro-symbolic approach for precision anti-reflux surgery.Updates in surgery · 2026Review
- DGCNet: a clinical knowledge-driven model for the segmentation of reflux esophagitis in endoscopic images.Gastroenterology report · 2026Article
- Digital health technologies in swallowing care from screening to rehabilitation: A narrative review.Auris, nasus, larynx · 2025Review
- Revolutionizing upper gastrointestinal disease diagnosis: The transformative role of artificial intelligence in endoscopy.World journal of gastrointestinal endoscopy · 2025Review
- Recent advances in machine learning for precision diagnosis and treatment of esophageal disorders.World journal of gastroenterology · 2025Review
- Constructing an artificial intelligence-assisted system for the assessment of gastroesophageal valve function based on the hill classification (with video).BMC medical informatics and decision making · 2025Article
- Early detection of esophageal cancer: Evaluating AI algorithms with multi-institutional narrowband and white-light imaging data.PloS one · 2025Article
- A Review of Application of Deep Learning in Endoscopic Image Processing.Journal of imaging · 2024Review
- Review
- Artificial intelligence system for the detection of Barrett's esophagus.World journal of gastroenterology · 2023Article
- Preparing Well for Esophageal Endoscopic Detection Using a Hybrid Model and Transfer Learning.Cancers · 2023Article
- Hydroxytyrosol and Its Potential Uses on Intestinal and Gastrointestinal Disease.International journal of molecular sciences · 2023Review
- Using deep learning to assess the function of gastroesophageal flap valve according to the Hill classification system.Annals of medicine · 2023Article
- An Improved Endoscopic Automatic Classification Model for Gastroesophageal Reflux Disease Using Deep Learning Integrated Machine Learning.Diagnostics (Basel, Switzerland) · 2022Article
- Artificial Intelligence in Digestive Endoscopy-Where Are We and Where Are We Going?Diagnostics (Basel, Switzerland) · 2022Review
- Review
- Artificial Intelligence in Endoscopy.Visceral medicine · 2021Review
- Improving the Classification Performance of Esophageal Disease on Small Dataset by Semi-supervised Efficient Contrastive Learning.Journal of medical systems · 2021Article
- Article
- [Use of Los Angeles Classification in Erosive Esophagitis: analysis of inter-observer and intra-observer agreement].Acta gastroenterologica LatinoamericanaArticle
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gastroesophageal reflux disease (GERD) is a common disease with high prevalence, and its endoscopic severity can be evaluated using the Los Angeles classification (LA grade). This paper proposes a deep learning model (i.e., GERD-VGGNet) that employs convolutional neural networks for automatic classification and interpretation of routine GERD LA grade. The proposed model employs a data augmentation technique, a two-stage no-freezing fine-tuning policy, and an early stopping criterion. As a result, the proposed model exhibits high generalizability. A dataset of images from 464 patients was used for model training and validation. An additional 32 patients served as a test set to evaluate the accuracy of both the model and our trainees. Experimental results demonstrate that the best model for the development set exhibited an overall accuracy of 99.2% (grade A-B), 100% (grade C-D), and 100% (normal group) using narrow-band image (NBI) endoscopy. On the test set, the proposed model resulted in an accuracy of 87.9%, which was significantly higher than the results of the trainees (75.0% and 65.6%). The proposed GERD-VGGNet model can assist automatic classification of GERD in conventional and NBI environments and thereby increase the accuracy of interpretation of the results by inexperienced endoscopists.
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