A cognitive approach to determine the benefits of pairing radiologists in mammogram reading

Ziba Gandomkar; Patrick C. Brennan; Claudia Mello-Thoms

doi:10.1117/12.2293364

7 March 2018 A cognitive approach to determine the benefits of pairing radiologists in mammogram reading

Ziba Gandomkar, Patrick C. Brennan, Claudia Mello-Thoms

Proceedings Volume 10577, Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment; 1057704 (2018) https://doi.org/10.1117/12.2293364
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Mammography screening in Europe and Australia is carried out by having two radiologists independently read the case and determine whether an actionable finding is present. If they disagree, a third radiologist – the arbitrator – reads the case and offers the final opinion. Currently radiologists are picked for the pair based on scheduling convenience, with no thought being given to whether a given pair of radiologists should really be put together to read cases. In the past research has shown that breast radiologists tend to commit the same mistakes time and again and incline to search mammograms in a particular way; hence, pairing two radiologists that tend to search a mammogram in an almost similar manner, for example, may not be such a good idea. In this study, we used eye position tracking to determine how radiologists searched a given set of cases. Using different cognitive models we paired the radiologists and determined the effect of the pairing on the radiologist’s performance using the Receivers Operating Characteristic Area Under the Curve (ROC AUC). Our results suggest that some pairings are detrimental to performance and should not be put together.

Conference Presentation

Citation Download Citation

Ziba Gandomkar, Patrick C. Brennan, and Claudia Mello-Thoms "A cognitive approach to determine the benefits of pairing radiologists in mammogram reading", Proc. SPIE 10577, Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment, 1057704 (7 March 2018); https://doi.org/10.1117/12.2293364

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