Paper
12 March 2009 Reading the lesson: eliciting requirements for a mammography training application
M. Hartswood, L. Blot, P. Taylor, S. Anderson, R. Procter, L. Wilkinson, L. Smart
Author Affiliations +
Abstract
Demonstrations of a prototype training tool were used to elicit requirements for an intelligent training system for screening mammography. The prototype allowed senior radiologists (mentors) to select cases from a distributed database of images to meet the specific training requirements of junior colleagues (trainees) and then provided automated feedback in response to trainees' attempts at interpretation. The tool was demonstrated to radiologists and radiographers working in the breast screening service at four evaluation sessions. Participants highlighted ease of selecting cases that can deliver specific learning objectives as important for delivering effective training. To usefully structure a large data set of training images we undertook a classification exercise of mentor authored free text 'learning points' attached to training case obtained from two screening centres (n=333, n=129 respectively). We were able to adduce a hierarchy of abstract categories representing classes of lesson that groups of cases were intended to convey (e.g. Temporal change, Misleading juxtapositions, Position of lesion, Typical/Atypical presentation, and so on). In this paper we present the method used to devise this classification, the classification scheme itself, initial user-feedback, and our plans to incorporated it into a software tool to aid case selection.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
M. Hartswood, L. Blot, P. Taylor, S. Anderson, R. Procter, L. Wilkinson, and L. Smart "Reading the lesson: eliciting requirements for a mammography training application", Proc. SPIE 7263, Medical Imaging 2009: Image Perception, Observer Performance, and Technology Assessment, 72631D (12 March 2009); https://doi.org/10.1117/12.813920
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CITATIONS
Cited by 3 scholarly publications.
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KEYWORDS
Mammography

Prototyping

Breast

Databases

Intelligence systems

Diagnostics

Pathology

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