Gastrointestinal (GI) diseases, including gastric and colorectal cancers, significantly impact global health, necessitating accurate and efficient diagnostic methods. Endoscopic examination is the primary diagnostic tool; however, its accuracy is limited by operator dependency and interobserver variability. Advancements in deep learning, particularly convolutional neural networks (CNNs), show great potential for enhancing GI disease detection and classification. This review explores the application of CNNs in endoscopic imaging, focusing on polyp and tumor detection, disease classification, endoscopic ultrasound, and capsule endoscopy analysis. We discuss the performance of CNN models with traditional diagnostic methods, highlighting their advantages in accuracy and real-time decision support. Despite promising results, challenges remain, including data availability, model interpretability, and clinical integration. Future directions include improving model generalization, enhancing explainability, and conducting large-scale clinical trials. With continued advancements, CNN-powered artificial intelligence systems could revolutionize GI endoscopy by enhancing early disease detection, reducing diagnostic errors, and improving patient outcomes.
Wang et al. (2025) studied this question.