|
PathEdEx – Uncovering high-explanatory visual diagnostics heuristics using digital pathology and multiscale gaze data
Dmitriy Shin1, Mikhail Kovalenko1, Ilker Ersoy1, Yu Li2, Donald Doll3, Chi-Ren Shyu4, Richard Hammer1
1 Department of Pathology and Anatomical Sciences; MU Informatics Institute, University of Missouri, Columbia, Missouri, USA
2 Department of Computer Science, University of Missouri, Columbia, Missouri, USA
3 Department of Medicine, University of Missouri, Columbia, Missouri, USA
4 MU Informatics Institute; Department of Computer Science, University of Missouri, Columbia, Missouri, USA
Correspondence Address:
Dmitriy Shin
Department of Pathology and Anatomical Sciences, University of Missouri, 1 Hospital Dr. M251 Pathology, Med Sci Bldg, Columbia, MO 65212
USA
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/jpi.jpi_29_17
|
|
Background: Visual heuristics of pathology diagnosis is a largely unexplored area where reported studies only provided a qualitative insight into the subject. Uncovering and quantifying pathology visual and nonvisual diagnostic patterns have great potential to improve clinical outcomes and avoid diagnostic pitfalls. Methods: Here, we present PathEdEx, an informatics computational framework that incorporates whole-slide digital pathology imaging with multiscale gaze-tracking technology to create web-based interactive pathology educational atlases and to datamine visual and nonvisual diagnostic heuristics. Results: We demonstrate the capabilities of PathEdEx for mining visual and nonvisual diagnostic heuristics using the first PathEdEx volume of a hematopathology atlas. We conducted a quantitative study on the time dynamics of zooming and panning operations utilized by experts and novices to come to the correct diagnosis. We then performed association rule mining to determine sets of diagnostic factors that consistently result in a correct diagnosis, and studied differences in diagnostic strategies across different levels of pathology expertise using Markov chain (MC) modeling and MC Monte Carlo simulations. To perform these studies, we translated raw gaze points to high-explanatory semantic labels that represent pathology diagnostic clues. Therefore, the outcome of these studies is readily transformed into narrative descriptors for direct use in pathology education and practice. Conclusion: PathEdEx framework can be used to capture best practices of pathology visual and nonvisual diagnostic heuristics that can be passed over to the next generation of pathologists and have potential to streamline implementation of precision diagnostics in precision medicine settings. |
