  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.
