Crowdsourcing for Identification of Polyp-Free Segments in Virtual Colonoscopy Videos

TL;DR

This study demonstrates that non-expert crowd workers can reliably identify polyp-free segments in virtual colonoscopy videos, potentially reducing radiologist reading time by 80%.

Contribution

It introduces a crowdsourcing framework for detecting polyp-free regions in VC videos, improving efficiency in medical diagnosis.

Findings

Crowd achieved 80% sensitivity in polyp detection.

86.5% specificity in identifying polyp-free segments.

Potential to skip 80% of video segments, saving time.

Abstract

Virtual colonoscopy (VC) allows a physician to virtually navigate within a reconstructed 3D colon model searching for colorectal polyps. Though VC is widely recognized as a highly sensitive and specific test for identifying polyps, one limitation is the reading time, which can take over 30 minutes per patient. Large amounts of the colon are often devoid of polyps, and a way of identifying these polyp-free segments could be of valuable use in reducing the required reading time for the interrogating radiologist. To this end, we have tested the ability of the collective crowd intelligence of non-expert workers to identify polyp candidates and polyp-free regions. We presented twenty short videos flying through a segment of a virtual colon to each worker, and the crowd was asked to determine whether or not a possible polyp was observed within that video segment. We evaluated our framework on Amazon Mechanical Turk and found that the crowd was able to achieve a sensitivity of 80.0% and specificity of 86.5% in identifying video segments which contained a clinically proven polyp. Since each polyp appeared in multiple consecutive segments, all polyps were in fact identified. Using the crowd results as a first pass, 80% of the video segments could in theory be skipped by the radiologist, equating to a significant time savings and enabling more VC examinations to be performed.