Paper
28 March 2013 Difficulty of mammographic cases in the context of resident training: preliminary experimental data
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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.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Maciej A. Mazurowski "Difficulty of mammographic cases in the context of resident training: preliminary experimental data", Proc. SPIE 8673, Medical Imaging 2013: Image Perception, Observer Performance, and Technology Assessment, 86730W (28 March 2013); https://doi.org/10.1117/12.2008550
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CITATIONS
Cited by 2 scholarly publications.
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KEYWORDS
Error analysis

Radiology

Mammography

Intelligence systems

Breast cancer

Computing systems

Imaging systems

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