Early detection of esophageal cancer: Evaluating AI algorithms with multi-institutional narrowband and white-light imaging data

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. The models demonstrated high performance, with RetinaNet achieving a precision of 98.4% and sensitivity of 91.3% in the NBI dataset, and YOLOv5 attaining a precision of 93.7% and sensitivity of 89.9% in the WLI dataset. The generalizability of these models was further validated using external data from multiple institutions. This study demonstrates an effective method for detecting esophageal tumors through AI-based esophageal endoscopic image analysis. These efforts are expected to significantly reduce misdiagnosis rates, enhance the effective diagnosis and treatment of esophageal cancer, and promote the standardization of medical services.