Document Detail


Distributed human intelligence for colonic polyp classification in computer-aided detection for CT colonography.
MedLine Citation:
PMID:  22274839     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
PURPOSE: To assess the diagnostic performance of distributed human intelligence for the classification of polyp candidates identified with computer-aided detection (CAD) for computed tomographic (CT) colonography.
MATERIALS AND METHODS: This study was approved by the institutional Office of Human Subjects Research. The requirement for informed consent was waived for this HIPAA-compliant study. CT images from 24 patients, each with at least one polyp of 6 mm or larger, were analyzed by using CAD software to identify 268 polyp candidates. Twenty knowledge workers (KWs) from a crowdsourcing platform labeled each polyp candidate as a true or false polyp. Two trials involving 228 KWs were conducted to assess reproducibility. Performance was assessed by comparing the area under the receiver operating characteristic curve (AUC) of KWs with the AUC of CAD for polyp classification.
RESULTS: The detection-level AUC for KWs was 0.845 ± 0.045 (standard error) in trial 1 and 0.855 ± 0.044 in trial 2. These were not significantly different from the AUC for CAD, which was 0.859 ± 0.043. When polyp candidates were stratified by difficulty, KWs performed better than CAD on easy detections; AUCs were 0.951 ± 0.032 in trial 1, 0.966 ± 0.027 in trial 2, and 0.877 ± 0.048 for CAD (P = .039 for trial 2). KWs who participated in both trials showed a significant improvement in performance going from trial 1 to trial 2; AUCs were 0.759 ± 0.052 in trial 1 and 0.839 ± 0.046 in trial 2 (P = .041).
CONCLUSION: The performance of distributed human intelligence is not significantly different from that of CAD for colonic polyp classification.
Authors:
Tan B Nguyen; Shijun Wang; Vishal Anugu; Natalie Rose; Matthew McKenna; Nicholas Petrick; Joseph E Burns; Ronald M Summers
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, N.I.H., Intramural; Research Support, Non-U.S. Gov't     Date:  2012-01-24
Journal Detail:
Title:  Radiology     Volume:  262     ISSN:  1527-1315     ISO Abbreviation:  Radiology     Publication Date:  2012 Mar 
Date Detail:
Created Date:  2012-02-23     Completed Date:  2012-04-27     Revised Date:  2013-06-26    
Medline Journal Info:
Nlm Unique ID:  0401260     Medline TA:  Radiology     Country:  United States    
Other Details:
Languages:  eng     Pagination:  824-33     Citation Subset:  AIM; IM    
Copyright Information:
© RSNA.
Affiliation:
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD 20892-1182, USA.
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MeSH Terms
Descriptor/Qualifier:
Aged
Algorithms
Area Under Curve
Colonic Polyps / radiography*
Colonography, Computed Tomographic / methods*
Female
Humans
Imaging, Three-Dimensional
Internet*
Male
Middle Aged
ROC Curve
Radiographic Image Interpretation, Computer-Assisted / methods*
Reproducibility of Results
Retrospective Studies
Software
Statistics, Nonparametric
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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