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Proceedings Paper

Difficulty of mammographic cases in the context of resident training: preliminary experimental data
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Paper Abstract

We are currently developing an intelligent data-driven educational system for mammography. Since our system attempts to predict which cases will be difficult for the trainees, it is important to better understand the concept of case difficulty. While the concept of difficulty is central to our efforts on adaptive education, its importance extends to radiology education in general as well as to image perception research. In this study, we tested some hypotheses that related to difficulty. Specifically, we performed a preliminary reader study to evaluate relationship between the error rate (an objective measure of difficulty), individual assessment of case difficulty by a resident and expert’s assessment of case difficulty (two subjective measures of difficulty). Furthermore, we investigated the relationship between individual and expert’s assessment of difficulty and time that the residents took to interpret the case. Time taken to interpret a case by a resident related well with the individual assessment of difficulty but its relationship with the expert’s assessment of difficulty was weaker. The analysis of the difficulty assessments showed that an increase in individual assessment of difficulty made by a resident relates well to an increase in his/her false positive errors but not to an increase in false negative errors. Interestingly, the expert’s assessment of difficulty was related to false negative errors in the trainees but not to false positive errors. These results offer additional guidance in our efforts to construct an adaptive education system as well as provide insight into important aspects of radiology education in general.

Paper Details

Date Published: 28 March 2013
PDF: 6 pages
Proc. SPIE 8673, Medical Imaging 2013: Image Perception, Observer Performance, and Technology Assessment, 86730W (28 March 2013); doi: 10.1117/12.2008550
Show Author Affiliations
Maciej A. Mazurowski, Carl E. Ravin Advanced Imaging Labs., Duke Univ. (United States)
Duke Univ. (United States)


Published in SPIE Proceedings Vol. 8673:
Medical Imaging 2013: Image Perception, Observer Performance, and Technology Assessment
Craig K. Abbey; Claudia R. Mello-Thoms, Editor(s)

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