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.
Ji Hwan Park, Seyedkoosha Mirhosseini, Saad Nadeem, Joseph Marino, Arie Kaufman, Kevin Baker, and Matthew Barish, "Crowdsourcing for identification of polyp-free segments in virtual colonoscopy videos," Proc. SPIE 10138, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications, 101380V (Presented at SPIE Medical Imaging: February 16, 2017; Published: 13 March 2017); https://doi.org/10.1117/12.2252281.
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