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.
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