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Proceedings Paper

Decision-making differences: novices, experts, and a neural network
Author(s): David Manning; Sam Bunting; John Leach
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Paper Abstract

We investigated the decision making performance of trained radiographers, novice radiographers and a neural network in the detection of fractures. Ground truth was established by the independent agreement of experienced radiologists for 740 single view digitized radiographs of the wrist. The images were categorized into negatives and positives; 520 of these were used to train the back propagation, three layer neural network in a supervised mode, and the remainder were used to create a test bank. The test was presented to 20 novice observers, 12 experienced radiographers trained in the detection of skeletal trauma and then to the trained neural network. ROC Az values for all the decision makers were not significantly different (p > 0.1) but there were significant differences in the values of True Positive and True Negative Fractions. The neural network showed a greater aptitude for distinguishing the normals. By filtering the neural net decisions through the human data we simulated the effect of assisted reporting. Results suggest that if fracture prevalence is very low in a population, a neural network demonstrating high specificity may have utility in reducing the number of images which must be reviewed by human experts.

Paper Details

Date Published: 14 April 2000
PDF: 10 pages
Proc. SPIE 3981, Medical Imaging 2000: Image Perception and Performance, (14 April 2000); doi: 10.1117/12.383107
Show Author Affiliations
David Manning, St. Martin's College (United Kingdom)
Sam Bunting, St. Martin's College (United Kingdom)
John Leach, Lancaster Univ. (United Kingdom)

Published in SPIE Proceedings Vol. 3981:
Medical Imaging 2000: Image Perception and Performance
Elizabeth A. Krupinski, Editor(s)

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