Work

Endoscopic Diagnosis of Eosinophilic Esophagitis Using a Multi-Task U-Net: A Pilot Study

Year: 2025

Type: article

Abstract: Purpose: Endoscopically identifying eosinophilic esophagitis (EoE) is difficult due to its rare incidence and subtle morphology.We aimed to develop a robust and accurate convolutional neural network (CNN) model for EoE identification and classification in endoscopic images. Materials and Methods:We collected 548 endoscopic images from 81 patients with EoE and 297 images from 37 normal patients.These datasets were labeled according to the four eosinophilic esophagitis endoscopic reference score (EREFS) features: edema, rings, exudates, and furrows.A multi-task U-Net with an auxiliary classifier on various levels of skip connections (scaU-Net) was proposed.Then, scaU-Net was compared with VGG19, ResNet50, EfficientNet-B3, and a typical multi-task U-Net CNN.The performances of each model were evaluated quantitatively and qualitatively based on accuracy (ACC), area under the receiver operating characteristics (AUROC), and gradient-weighted class activation map (Grad-CAM), and were also compared with those of 25 human endoscopists.Results: Our sca4U-Net with 4th-level skip connection showed the best performances in ACC (86.9%),AUROC (0.93), and outstanding Grad-CAM results compared to other models, reflecting the importance of utilizing the deepest skip connection.Moreover, the sca4U-Net showed generally better performance when compared with endoscopists with various levels of experience.Conclusion: Our method showed robust performance compared to expert endoscopists and could assist endoscopists of all experience levels in the early detection of EoE-a rare but clinically important condition.

Source: Yonsei Medical Journal

DOI: 10.3349/ymj.2024.0404