July 2013
Volume 13, Issue 9
Free
Vision Sciences Society Annual Meeting Abstract  |   July 2013
Are Radiologists Ideal Observers? --Evidence from Observer Studies in Radiology
Author Affiliations
  • Xin He
    Food and Drug Administration
  • Brandon Gallas
    Food and Drug Administration
  • Frank Samuelson
    Food and Drug Administration
  • Berkman Sahiner
    Food and Drug Administration
  • Kyle Myers
    Food and Drug Administration
Journal of Vision July 2013, Vol.13, 751. doi:10.1167/13.9.751
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      Xin He, Brandon Gallas, Frank Samuelson, Berkman Sahiner, Kyle Myers; Are Radiologists Ideal Observers? --Evidence from Observer Studies in Radiology. Journal of Vision 2013;13(9):751. doi: 10.1167/13.9.751.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

In the past fifty years, a large number of ROC curves have been generated by radiologists reading medical images. There is a common belief that human ROC curves should generally be convex and that non-convex ROC curves are caused by some imperfections in the measurement procedures, for example, the limited number of images read by observers, poor experimental design and/or curve fitting errors. We propose an ideal observer (IO) model to describe a radiologist’s performance based on the fact that a convex ROC curve corresponds to one unique pair of likelihood ratio (LR) distributions. We define an equivalent IO (EIO) as the one who has the same performance as the human observer, as characterized by an ROC curve. To measure the LR distributions of the EIO, we formalize experimental design principles that force the observers to act rationally based on von Neumann and Morgenstern’s axioms. We show that human observer study design refinements in radiology, although motivated by empirical or practical principles, implicitly enforce rationality and result in reasonably convex ROC curves. EIO theory allows us to model the human observer as an ideal observer. It also suggests that while the human observers are not optimal or cannot fully use the statistical information in medical images, at some level they can be rational or ideal in using the limited information that they are able to extract from the images for decision making.

Meeting abstract presented at VSS 2013

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