Endoscopic Video

Endoscopic video analysis is rapidly advancing, driven by the need for automated and objective assessment of medical images from various endoscopic procedures. Current research focuses on developing robust deep learning models, including transformers and convolutional neural networks (CNNs) like YOLO and U-Net variants, to address tasks such as image stitching, disease severity assessment, instrument localization, and artifact detection. These advancements aim to improve diagnostic accuracy, streamline workflows, and enhance the safety and efficiency of minimally invasive surgeries and other endoscopic procedures. The resulting improvements in image processing and analysis have significant implications for both clinical practice and medical research.