Construction of an artificial intelligence system for the Los Angeles classification-based assessment of reflux esophagitis (with video)

Background This study aims to develop an artificial intelligence system capable of automatically classifying endoscopic images of reflux esophagitis (RE) according to the Los Angeles (LA) classification, thereby improving the accuracy and efficiency of RE diagnosis and providing intelligent support for clinical decision-making. Methods RE images from three centers were collected to construct a dataset for training, validating, and testing a deep learning model. Model performance was evaluated using metrics such as accuracy, sensitivity, specificity, precision, area under the receiver operating characteristic curve (AUC), and F1 score. After model training, Grad-CAM (Gradient-weighted Class Activation Mapping) visualization techniques were applied to enhance model transparency. Finally, a clinical application was developed using PyQt5 technology for portable use. Results Among the five models evaluated, YOLOv11l demonstrated the best performance, achieving an accuracy, precision, sensitivity, and F1 score of 97.89%, 94.90%, 93.69%, and 94.28% on the validation set, respectively; and a weighted average accuracy, precision, specificity, and AUC of 96.26%, 91.58%, 98.04%, and 0.995 on the test set. The diagnostic accuracy of this model was significantly higher than that of both junior (χ 2 =45.93, P<0.05) and senior endoscopists (χ 2 =8.34, P<0.05). Conclusions The artificial intelligence model and application developed based on the YOLOv11 network can rapidly and accurately grade the severity of RE according to the LA classification on retrospective external test data, providing a promising proof-of-concept system that warrants further prospective and multi-reader validation before routine clinical deployment.