Abstract / Summary
Abstract Background: Cytomegalovirus (CMV) and herpes simplex virus (HSV) are the most common causes of infectious esophagitis in immuno-compromised patients. However, their endoscopic features frequently overlap, making real-time etiologic differentiation difficult and often requiring delayed confirmation by immunohistochemistry. Methods: We developed and validated a deep learning model to distinguish CMV from HSV esophagitis using endoscopic images from biopsy-proven cases at a tertiary referral center. The model was trained with domain-specific pretraining and a curriculum learning strategy that sequentially introduced cases according to diagnostic difficulty, mimicking clinical learning processes. Diagnostic performance was evaluated using an independent test set and compared with experienced endoscopists. Results: The curriculum learning–based model demonstrated improved classification performance over conventional training approaches, achieving an AUROC of 0.783 (95% confidence interval 0.680–0.885). Its sensitivity and specificity were comparable to those of expert endoscopists. Model visualization showed attention to clinically relevant mucosal abnormalities. Conclusion: Deep learning analysis of routine endoscopic images can assist in differentiating CMV and HSV esophagitis. A curriculum learning strategy may enhance clinical applicability by improving performance in visually ambiguous conditions, potentially supporting earlier therapeutic decision-making while awaiting histopathologic confirmation