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.
