Abstract
Esophageal cancer is one of the most common cancers worldwide, especially esophageal squamous cell carcinoma, which is often diagnosed at a late stage and has a poor prognosis. This study aimed to develop an algorithm to detect tumors in esophageal endoscopy images using innovative artificial intelligence (AI) techniques for early diagnosis and detection of esophageal cancer. We used white light and narrowband imaging data collected from Gachon University Gil Hospital, and applied YOLOv5 and RetinaNet detection models to detect lesions. We also verified the generalizability of the models using external data collected from various institutions. The results showed that the AI models showed high precision and sensitivity in white light and narrowband images, and the RetinaNet model performed well, analyzing not only polyps but also superficial esophageal cancer. This study demonstrates an effective method for detecting esophageal tumors through AI-based esophageal endoscopic image analysis and is expected to help researchers develop AI models that combine real-time image-based detection and synthesis algorithms to predict the depth of lesion invasion. These results and efforts are expected to significantly reduce misdiagnosis rates, enhance the effective diagnosis and treatment of esophageal cancer, and promote the standardization of medical services.