Paper
6 March 2008 Individualised training to address variability of radiologists' performance
Shanghua Sun, Paul Taylor, Louise Wilkinson, Lisanne Khoo
Author Affiliations +
Abstract
Computer-based tools are increasingly used for training and the continuing professional development of radiologists. We propose an adaptive training system to support individualised learning in mammography, based on a set of real cases, which are annotated with educational content by experienced breast radiologists. The system has knowledge of the strengths and weakness of each radiologist's performance: each radiologist is assessed to compute a profile showing how they perform on different sets of cases, classified by type of abnormality, breast density, and perceptual difficulty. We also assess variability in cognitive aspects of image perception, classifying errors made by radiologists as errors of search, recognition or decision. This is a novel element in our approach. The profile is used to select cases to present to the radiologist. The intelligent and flexible presentation of these cases distinguishes our system from existing training tools. The training cases are organised and indexed by an ontology we have developed for breast radiologist training, which is consistent with the radiologists' profile. Hence, the training system is able to select appropriate cases to compose an individualised training path, addressing the variability of the radiologists' performance. A substantial part of the system, the ontology has been evaluated on a large number of cases, and the training system is under implementation for further evaluation.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shanghua Sun, Paul Taylor, Louise Wilkinson, and Lisanne Khoo "Individualised training to address variability of radiologists' performance", Proc. SPIE 6917, Medical Imaging 2008: Image Perception, Observer Performance, and Technology Assessment, 69170G (6 March 2008); https://doi.org/10.1117/12.770296
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CITATIONS
Cited by 8 scholarly publications.
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KEYWORDS
Breast

Mammography

Databases

Diagnostics

Breast cancer

Health informatics

Medical imaging

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