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What do we know about volumetric medical image interpretation?: a review of the basic science and medical image perception literatures.

Lauren H WilliamsTrafton Drew
Published in: Cognitive research: principles and implications (2019)
Interpretation of volumetric medical images represents a rapidly growing proportion of the workload in radiology. However, relatively little is known about the strategies that best guide search behavior when looking for abnormalities in volumetric images. Although there is extensive literature on two-dimensional medical image perception, it is an open question whether the conclusions drawn from these images can be generalized to volumetric images. Importantly, volumetric images have distinct characteristics (e.g., scrolling through depth, smooth-pursuit eye-movements, motion onset cues, etc.) that should be considered in future research. In this manuscript, we will review the literature on medical image perception and discuss relevant findings from basic science that can be used to generate predictions about expertise in volumetric image interpretation. By better understanding search through volumetric images, we may be able to identify common sources of error, characterize the optimal strategies for searching through depth, or develop new training and assessment techniques for radiology residents.
Keyphrases
  • deep learning
  • artificial intelligence
  • convolutional neural network
  • optical coherence tomography
  • healthcare
  • machine learning
  • systematic review
  • public health
  • mass spectrometry
  • drinking water
  • high speed