Novel artificial intelligence-driven software significantly shortens the time required for annotation in computer vision projects

Background and study aims The contribution of artificial intelligence (AI) to endoscopy is rapidly expanding. Accurate labelling of source data (video frames) remains the rate-limiting step for such projects and is a painstaking, cost-inefficient, time-consuming process. A novel software platform, Cord Vision (CdV) allows automated annotation based on “embedded intelligence.” The user manually labels a representative proportion of frames in a section of video (typically 5 %), to create ‘micro-modelsʼ which allow accurate propagation of the label throughout the remaining video frames. This could drastically reduce the time required for annotation. Methods We conducted a comparative study with an open-source labelling platform (CVAT) to determine speed and accuracy of labelling. Results Across 5 users, CdV resulted in a significant increase in labelling performance ( P < 0.001) compared to CVAT for bounding box placement. Conclusions This advance represents a valuable first step in AI-image analysis projects.