Gastrointestinal Disease Detection

Gastrointestinal (GI) disease detection is rapidly advancing through the application of artificial intelligence (AI), primarily focusing on automating the analysis of endoscopic images and video capsule endoscopy (VCE) data. Current research emphasizes the development and validation of deep learning models, including convolutional neural networks (CNNs) and vision transformers, for improved accuracy and efficiency in identifying various GI disorders, particularly bleeding and polyps. The availability of large, annotated datasets is crucial for training these models and overcoming challenges like low-light conditions in VCE. Ultimately, these AI-driven tools aim to enhance diagnostic accuracy, reduce human error, and improve patient outcomes in gastroenterology.