Development and validation of a visual grading scale for assessing image quality of AP pelvis radiographic images

Br J Radiol. 2016;89(1061):20150430. doi: 10.1259/bjr.20150430. Epub 2016 Mar 4.

Abstract

Objective: The aim of this article was to apply psychometric theory to develop and validate a visual grading scale for assessing the visual perception of digital image quality anteroposterior (AP) pelvis.

Methods: Psychometric theory was used to guide scale development. Seven phantom and seven cadaver images of visually and objectively predetermined quality were used to help assess scale reliability and validity. 151 volunteers scored phantom images, and 184 volunteers scored cadaver images. Factor analysis and Cronbach's alpha were used to assess scale validity and reliability.

Results: A 24-item scale was produced. Aggregated mean volunteer scores for each image correlated with the rank order of the visually and objectively predetermined image qualities. Scale items had good interitem correlation (≥0.2) and high factor loadings (≥0.3). Cronbach's alpha (reliability) revealed that the scale has acceptable levels of internal reliability for both phantom and cadaver images (α = 0.8 and 0.9, respectively). Factor analysis suggested that the scale is multidimensional (assessing multiple quality themes).

Conclusion: This study represents the first full development and validation of a visual image quality scale using psychometric theory. It is likely that this scale will have clinical, training and research applications.

Advances in knowledge: This article presents data to create and validate visual grading scales for radiographic examinations. The visual grading scale, for AP pelvis examinations, can act as a validated tool for future research, teaching and clinical evaluations of image quality.

Publication types

  • Validation Study

MeSH terms

  • Cadaver
  • Factor Analysis, Statistical
  • Humans
  • Pelvis / diagnostic imaging*
  • Psychometrics
  • Quality Assurance, Health Care / statistics & numerical data*
  • Radiographic Image Interpretation, Computer-Assisted
  • Reproducibility of Results
  • Surveys and Questionnaires