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

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

Journal

Health SA = SA Gesondheid
ISSN: 1025-9848
Titre abrégé: Health SA
Pays: South Africa
ID NLM: 101213385

Informations de publication

Date de publication:
2019
Historique:
received: 11 04 2019
accepted: 12 09 2019
entrez: 15 1 2020
pubmed: 15 1 2020
medline: 15 1 2020
Statut: epublish

Résumé

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. 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. The setting was a university that offers MI education. 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. 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. The results of this study are essential in guiding radiography educators in designing CAD tools for teaching chest radiography pattern recognition.

Sections du résumé

BACKGROUND 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 OBJECTIVE
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 METHODS
The setting was a university that offers MI education.
METHOD METHODS
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 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 CONCLUSIONS
The results of this study are essential in guiding radiography educators in designing CAD tools for teaching chest radiography pattern recognition.

Identifiants

pubmed: 31934444
doi: 10.4102/hsag.v24i0.1322
pii: HSAG-24-1322
pmc: PMC6917372
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1322

Informations de copyright

© 2019. The Authors.

Déclaration de conflit d'intérêts

The authors declare that no competing interests exist.

Références

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Auteurs

Sibusiso Mdletshe (S)

Department of Medical Imaging and Radiation Sciences, Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa.

Andre L Nel (AL)

Department of Mechanical Engineering Science, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa.

Louise Rainford (L)

School of Medicine, Faculty of Health Sciences, University College Dublin, Dublin, Ireland.

Heather A Lawrence (HA)

Department of Medical Imaging and Radiation Sciences, Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa.

Classifications MeSH