Early gastric cancer (EGC) is often difficult to detect accurately during endoscopic examination due to subtle morphological features and variability among endoscopists. Artificial intelligence (AI) has shown promise in improving diagnostic performance; however, most existing models lack interpretability and rely on single-modality imaging.
This study aims to develop and evaluate an explainable multimodal artificial intelligence model for the diagnosis of early gastric cancer using endoscopic imaging. The model integrates features derived from white-light imaging and image-enhanced endoscopy, along with quantitative image features and clinical data, to improve diagnostic accuracy and provide interpretable decision support.
The primary outcome is the diagnostic performance of the AI model for detecting early gastric cancer, evaluated by area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity.
The results of this study are expected to provide evidence for the clinical utility of explainable AI in endoscopic diagnosis and support the development of reliable human-AI collaborative diagnostic systems.