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

• Published in: Health SA = SA Gesondheid Vol. 24; no. 1; pp. 1 - 7
• Main Authors: Mdletshe, Sibusiso, Rainford, Louise, Nel, Andre L., Lawrence, Heather A.
• Format: Journal Article
• Language: English
• Published: South Africa AOSIS 2019; AOSIS (Pty) Ltd; AOSIS Publishing on behalf of University of Johannesburg
• Subjects: Chest radiography; Computer-aided instruction; Data analysis; Data collection; Design; Focus groups; Health Care Sciences & Services; Health Policy & Services; Implicit skills; Interviews; Medical imaging; Original Research; Pattern recognition; Radiography; Simulated learning; Skills; Students; Teaching; Tutoring; chest radiography; implicit skills; simulated learning; computer-aided instruction; pattern recognition
• ISSN: 1025-9848; 2071-9736; 2071-9736
• DOI: 10.4102/hsag.v24i0.1322

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.