Original Research

Computer-aided detection tool development for teaching chest radiograph pattern recognition to undergraduate radiography students: A context needs and capability analysis

Sibusiso Mdletshe, Andre L. Nel, Louise Rainford, Heather A. Lawrence
Health SA Gesondheid | Vol 24 | a1322 | DOI: https://doi.org/10.4102/hsag.v24i0.1322 | © 2019 Sibusiso Mdletshe, Andre L Nel, Louise Rainford, Heather Anne Lawrence | This work is licensed under CC Attribution 4.0
Submitted: 11 April 2019 | Published: 15 October 2019

About the author(s)

Sibusiso Mdletshe, Department of Medical Imaging and Radiation Sciences, Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa
Andre L. Nel, Department of Mechanical Engineering Science, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa
Louise Rainford, School of Medicine, Faculty of Health Sciences, University College Dublin, Dublin, Ireland
Heather A. Lawrence, Department of Medical Imaging and Radiation Sciences, Faculty of Health Sciences, University of Johannesburg, Johannesburg, United Kingdom

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Background: Medical imaging (MI) education has experienced a shift aligned with the advances in technology and the role played by radiographers in pattern recognition. This has led to increased use of technology-enhanced teaching and simulated learning approaches (e.g. computer-aided detection [CAD] tools) which also support the increasing requirement to develop pattern-recognition skills at undergraduate level. However, the development of these approaches need to be explored and planned carefully to be context-relevant.

Aim: The aim of this study was to explore and describe the need for and capability of a CAD tool for teaching chest radiography pattern recognition in an undergraduate radiography programme.

Setting: The setting was a university that offers MI education.

Method: The study employed a qualitative descriptive design with an interpretive research paradigm. Purposive sampling was used to recruit information-rich participants for a focus group interview. Information-rich participants were considered to be those who were involved in teaching clinical skills, such as those required in pattern recognition, to radiography students. Data were transcribed verbatim and analysed in a step-by-step approach.

Results: Three main themes emerged: (1) a structured approach to enhance implicit skills is critical in the CAD tool design; (2) an authentic tool which is able to simulate real-world experiences in image analysis is essential; and (3) a tool which encourages self-directed learning using a wide variety of pathological conditions would be ideal.

Conclusion: The results of this study are essential in guiding radiography educators in designing CAD tools for teaching chest radiography pattern recognition.


computer-aided instruction; implicit skills; pattern recognition; chest radiography; simulated learning


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