CC BY-NC-ND 4.0 · Endosc Int Open 2021; 09(04): E621-E626
DOI: 10.1055/a-1341-0689
Original article

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

Ulrik Stig Hansen
1  Cord Technologies Ltd, London, England, NW1 6NE
,
Eric Landau
1  Cord Technologies Ltd, London, England, NW1 6NE
,
Mehul Patel
2  Kingʼs Health Partners Institute of Therapeutic Endoscopy, Kingʼs College Hospital NHS Foundation Trust, London SE5 9RS, United Kingdom
,
BuʼHussain Hayee
2  Kingʼs Health Partners Institute of Therapeutic Endoscopy, Kingʼs College Hospital NHS Foundation Trust, London SE5 9RS, United Kingdom
› Author Affiliations

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.



Publication History

Received: 18 September 2020

Accepted: 02 December 2020

Publication Date:
14 April 2021 (online)

© 2021. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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