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Expertise development in volumetric image interpretation of radiology residents: what do longitudinal scroll data reveal?

Dorien van MontfortEllen M KokKoen VinckenMarieke van der SchaafAnouk van der GijpCécile RaveslootDirk Rutgers
Published in: Advances in health sciences education : theory and practice (2020)
The current study used theories on expertise development (the holistic model of image perception and the information reduction hypothesis) as a starting point to identify and explore potentially relevant process measures to monitor and evaluate expertise development in radiology residency training. It is the first to examine expertise development in volumetric image interpretation (i.e., CT scans) within radiology residents using scroll data collected longitudinally over five years of residency training. Consistent with the holistic model of image perception, the percentage of time spent on full runs, i.e. scrolling through more than 50% of the CT-scan slices (global search), decreased within residents over residency training years. Furthermore, the percentage of time spent on question-relevant areas in the CT scans increased within residents over residency training years, consistent with the information reduction hypothesis. Second, we examined if scroll patterns can predict diagnostic accuracy. The percentage of time spent on full runs and the percentage of time spent on question-relevant areas did not predict diagnostic accuracy. Thus, although scroll patterns over training years are consistent with visual expertise theories, they could not be used as predictors of diagnostic accuracy in the current study. Therefore, the relation between scroll patterns and performance needs to be further examined, before process measures can be used to monitor and evaluate expertise development in radiology residency training.
Keyphrases
  • computed tomography
  • artificial intelligence
  • deep learning
  • virtual reality
  • dual energy
  • contrast enhanced
  • image quality
  • healthcare
  • medical students
  • magnetic resonance
  • social media
  • health information