We would like to thank Dr Vikas Chaudhary and Dr Shahina Bano for their interest and encouraging comments regarding our recently published article . We also appreciate the opportunity to discuss and clarify some points of possible interest, concerning this article and a previous referenced article , for the readers.
As mentioned, we have distinguished between altering prevalence expectations by interpreting an identical set of images rather than changing the actual target prevalence with different sets of images as has been the norm in previous studies . Our results have found no evidence that the accuracy of experienced radiologists is altered because of changing explicit abnormality expectation rates. However, confidence ratings and visual search appear to be affected, and this impact appears more apparent for normal images. Also, although a previous study by our group of naive readers did not provide evidence that the overall performance of the student radiographers was affected by the changing prevalence expectations in terms of overall receiver operating characteristic (ROC) (Az) measurement, a trend was observed where their sensitivity increased and their specificity decreased at higher abnormality prevalence expectations . Interestingly, it has been shown elsewhere that false negatives tend to decrease at higher prevalence because the primary effect of prevalence results in a criterion shift where an observer in a higher prevalence situation is more likely to call an ambiguous finding as positive and often less likely to terminate visual search .
We believe that a critical question for future research is to determine the extent that prevalence expectation affects the detectability of abnormalities when presenting the same case sets to radiologists but rather than stating actual prevalence values as we have used here, present the cases as belonging to specific patient prevalence conditions, such as tuberculosis, as suggested by Dr Vikas Chaudhary and Dr Shahina Bano. It is clear that further work is required to understand the impact of prevalence expectation on different types of images and readers (eg, “learning radiologists”) to fully understand the context of image interpretation in perceived high and low prevalence conditions.
References
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