Gastroenterology and Artificial Intelligence: 2nd Annual Artificial Intelligence Summit | October 2020-Computer Vision A Primer for the Gastroenterlogist

Computer Vision A Primer for the Gastroenterlogist

Pdf Summary

This document provides a primer on computer vision for gastroenterologists. Computer vision is a subset of artificial intelligence (AI) that allows computers to "see" and interpret visual content. AI, in general, refers to computer systems that can perform tasks that typically require human intelligence. Machine learning and deep learning are two techniques used in computer vision. Machine learning enables systems to automatically learn and improve from experience without explicit programming, while deep learning relies on networks capable of learning from unstructured or unlabeled data.<br /><br />Computer vision in gastroenterology has made significant progress due to AI techniques. Some key tasks in computer vision include classification, localization, object detection, and semantic segmentation. Classification involves classifying a single object in an image, localization finds the object and draws a bounding box around it, object detection classifies and detects all objects in an image, and semantic segmentation classifies every pixel in the image. These tasks have practical applications in analyzing gastrointestinal images, such as identifying polyps, diverticula, and dysplasia.<br /><br />While AI has shown promise in computer vision, there are challenges to overcome. Image classification can be fallible, and defining certainty is complex. However, there have been dramatic advances in AI and computer vision in recent years, with examples like YOLO for object detection. Computer vision and AI can also be used for quality assessment in endoscopy, including assessing image clarity, preparation quality, and distension.<br /><br />To further advance computer vision in gastroenterology, several key areas need attention. This includes identifying priority use cases, data science for obtaining, curating, storing, and labeling endoscopic images and videos, and fostering collaboration between clinicians and AI/computer vision experts. Image labeling is critical for AI and computer vision, and labor-intensive image labeling is an essential component of these technologies.<br /><br />In conclusion, computer vision has great potential in gastroenterology, but further research and collaboration are needed to fully realize its benefits.

Keywords

computer vision

gastroenterologists

artificial intelligence

machine learning

deep learning

classification

localization

object detection

semantic segmentation

endoscopy