Development of Deep Learning-Based Virtual Lugol Chromoendoscopy for Superficial Esophageal Squamous Cell Carcinoma

Lugol chromoendoscopy has been shown to increase the sensitivity of detection of esophageal squamous cell carcinoma (ESCC). We aimed to develop a deep learning-based virtual lugol chromoendoscopy (V-LCE) method. We developed still V-LCE images for superficial ESCC using a cycle-consistent generative adversarial network (CycleGAN). Six endoscopists graded the detection and margins of ESCCs using white-light endoscopy (WLE), real lugol chromoendoscopy (R-LCE), and V-LCE on a five-point scale ranging from 1 (poor) to 5 (excellent). We also calculated and compared the color differences between cancerous and non-cancerous areas using WLE, R-LCE, and V-LCE. Scores for the detection and margins were significantly higher with R-LCE than V-LCE (detection, 4.7 vs. 3.8, respectively; p