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Related Experiment Video

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PathEdEx - Uncovering High-explanatory Visual Diagnostics Heuristics Using Digital Pathology and Multiscale Gaze

Dmitriy Shin1,2, Mikhail Kovalenko1,2, Ilker Ersoy1,2

  • 1Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, Missouri, USA.

Journal of Pathology Informatics
|August 23, 2017
PubMed
Summary

PathEdEx, a new framework, quantifies visual diagnostic patterns in pathology using gaze-tracking technology. This approach captures expert heuristics to improve diagnostic accuracy and train future pathologists.

Keywords:
Digital pathologyeye trackinggaze trackingpathology diagnosisvisual heuristicsvisual knowledgewhole slide images

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Area of Science:

  • Digital pathology
  • Computational pathology
  • Medical informatics

Background:

  • Visual diagnostic heuristics in pathology remain largely unquantified, limiting clinical outcomes and potentially leading to diagnostic errors.
  • Existing studies offer only qualitative insights into the visual decision-making processes of pathologists.

Purpose of the Study:

  • To introduce PathEdEx, a computational framework for analyzing visual and non-visual diagnostic heuristics in digital pathology.
  • To develop web-based interactive atlases for pathology education and datamining diagnostic patterns.

Main Methods:

  • PathEdEx integrates whole-slide digital pathology images with multiscale gaze-tracking technology.
  • Quantitative analysis of expert and novice zooming/panning dynamics was performed.
  • Association rule mining and Markov chain modeling were used to identify diagnostic factors and expertise-based strategy differences.

Main Results:

  • The framework successfully mined visual and non-visual diagnostic heuristics from a hematopathology atlas.
  • Differences in diagnostic strategies between experts and novices were identified using gaze data translated into semantic labels.
  • Quantitative insights into time dynamics of diagnostic operations were obtained.

Conclusions:

  • The PathEdEx framework captures and quantifies expert pathology diagnostic heuristics for educational and practical applications.
  • This approach has the potential to enhance pathology training and streamline precision diagnostics in precision medicine.