Wang, Chi-ChihChi-ChihWangYu-Ching ChiuWei-Liang ChenTzu-Wei YangTsai, Ming-ChangMing-ChangTsaiMing-Hseng Tseng2025-12-102025-12-102021-03-0210.3390/ijerph18052428https://scholars.csmu.edu.tw/handle/123456789/402<jats:p>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.</jats:p>engastroesophageal reflux disease classificationartificial intelligencedeep learningconventional endoscopynarrow-band image[SDGs]SDG3journal-article