Journal:PathEdEx – Uncovering high-explanatory visual diagnostics heuristics using digital pathology and multiscale gaze data
| Full article title | PathEdEx – Uncovering high-explanatory visual diagnostics heuristics using digital pathology and multiscale gaze data |
|---|---|
| Journal | Journal of Pathology Informatics |
| Author(s) | Shin, Dmitriy; Kovalenko, Mikhail; Ersoy, Ilker; Li, Yu; Doll, Donald; Shyu, Chi-Ren; Hammer, Richard |
| Author affiliation(s) | University of Missouri – Columbia |
| Primary contact | Email: Available w/ login |
| Year published | 2017 |
| Volume and issue | 8 |
| Page(s) | 29 |
| DOI | 10.4103/jpi.jpi_29_17 |
| ISSN | 2153-3539 |
| Distribution license | http://www.jpathinformatics.org |
| Download | http://www.jpathinformatics.org/temp/JPatholInform8129-6283361_172713.pdf (PDF) |
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Abstract
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 non-visual 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 non-visual diagnostic heuristics.
Results: We demonstrate the capabilities of PathEdEx for mining visual and non-visual 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: The PathEdEx framework can be used to capture best practices of pathology visual and non-visual 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.
Keywords: Digital pathology, eye tracking, gaze tracking, pathology diagnosis, visual heuristics, visual knowledge, whole slide images
Introduction
Pathology diagnosis is a highly complex process where multiple clinical and diagnostic factors have to be taken into account in an iterative fashion to produce a plausible conclusion that most accurately explains these factors from a biological standpoint.[1] Information from patient clinical history; morphological findings from microscopic evaluation of biopsies, aspirates, and smears; as well as data from flow cytometry, immunohistochemistry (IHC), and "omics" modalities such as comparative hybridization arrays and next-generation sequencing are used in the diagnostic process, which currently can be described more like a subjective exercise than a well-defined protocol. As such, it can frequently lead to diagnostic pitfalls which may harmfully impact patient case with a wrong diagnosis. The diagnostic pitfalls are most often encountered in the case of complex diseases such as cancer[2], where multiple fine-grained clinical phenotypes may require a better understanding of genomic variations across patient populations and therefore require better protocols for pathology diagnosis. It is especially important in light of realizing precision medicine ideas.[3] A better understanding of pathology diagnosis, especially of heuristics of visual reasoning over microscopic slides, is crucial to develop means for new genomic-enabled precision diagnostics methods.
References
- ↑ Shin, D.; Arthur, G.; Caldwell, C. et al. (2012). "A pathologist-in-the-loop IHC antibody test selection using the entropy-based probabilistic method". Journal of Pathology Informatics 3: 1. doi:10.4103/2153-3539.93393. PMC PMC3307231. PMID 22439121. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3307231.
- ↑ Higgins, R.A.; Blankenship, J.E.; Kinney, M.C. (2008). "Application of immunohistochemistry in the diagnosis of non-Hodgkin and Hodgkin lymphoma". Archives of Pathology & Laboratory Medicine 132 (3): 441-61. doi:10.1043/1543-2165(2008)132[441:AOIITD]2.0.CO;2. PMID 18318586.
- ↑ Tenenbaum, J.D.; Avillach, P.; Benham-Hutchins, M. et al. (2016). "An informatics research agenda to support precision medicine: Seven key areas". JAMIA 23 (4): 791-5. doi:10.1093/jamia/ocv213. PMC PMC4926738. PMID 27107452. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4926738.
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.
