Enteroscope Biopsy

Enteroscope biopsy analysis is rapidly advancing through the application of artificial intelligence, primarily focusing on improving the accuracy and efficiency of colorectal cancer diagnosis. Current research utilizes deep learning models, such as convolutional neural networks and U-Net architectures, to analyze histopathological images from enteroscope biopsies, automating tasks like cell counting (e.g., eosinophils) and classifying tumor differentiation stages. These advancements offer the potential for earlier and more precise diagnoses, leading to improved patient outcomes and more effective clinical trial stratification by enabling objective, quantitative assessment of disease features previously reliant on subjective pathologist interpretation.