Abstract
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.
