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Modelling digital health data: The ExaMode ontology for computational pathology.

Laura Menotti1, Gianmaria Silvello1, Manfredo Atzori2,3

  • 1Department of Information Engineering, University of Padua, Padova, Italy.

Journal of Pathology Informatics
|September 14, 2023
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Summary

The ExaMode ontology standardizes digital pathology data, enabling better image analysis and data integration for cancer and celiac disease research. This semantic layer facilitates automatic annotation and knowledge extraction from histopathology reports.

Keywords:
Computational pathologyHistopathologyOntologySemantic integration

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Area of Science:

  • Digital Pathology
  • Medical Informatics
  • Ontology Engineering

Background:

  • Computational pathology requires standardized nomenclature for knowledge extraction from annotated image datasets.
  • Data annotation in digital pathology faces challenges like label heterogeneity, multilingualism, and diverse clinical practices, hindering dataset reuse.
  • A shared model is needed to overcome data variability and integration problems in digital pathology.

Purpose of the Study:

  • To present the ExaMode ontology, a novel semantic model for histopathology.
  • To standardize nomenclature and facilitate knowledge extraction in digital pathology.
  • To enable better data integration and analysis of multimodal histopathology data.

Main Methods:

  • The ExaMode ontology was developed bottom-up with iterative feedback from pathologists and clinicians.
  • It models the histopathology process for colon, cervical, and lung tumors, and celiac disease.
  • The ontology is organized into 5 semantic areas, providing a template for various histopathology diseases.

Main Results:

  • The ExaMode ontology serves as a common semantic layer for tools including entity linking, collaborative annotation, and multimodal data integration platforms.
  • It enables data storage in graph databases using the RDF model, facilitating the development of advanced image analysis algorithms.
  • Seamless data integration and unified query access via SPARQL enable extraction of clinical insights.

Conclusions:

  • The ExaMode ontology addresses key challenges in digital pathology by providing a standardized semantic framework.
  • Its application supports improved data annotation, integration, and analysis, leading to more accurate computational pathology algorithms.
  • This ontology facilitates the extraction of valuable clinical insights from heterogeneous histopathology data.