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

Autor:	Menotti L; Department of Information Engineering, University of Padua, Padova, Italy., Silvello G; Department of Information Engineering, University of Padua, Padova, Italy., Atzori M; Information Systems Institute, University of Applied Sciences Western Switzerland, Delémont, Switzerland.; Department of Neuroscience, University of Padua, Padova, Italy., Boytcheva S; Sirma AI, Sofia, Bulgaria., Ciompi F; Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands., Di Nunzio GM; Department of Information Engineering, University of Padua, Padova, Italy., Fraggetta F; Pathology Unit Gravina Hospital Caltagirone ASP, Caltagirone, Italy., Giachelle F; Department of Information Engineering, University of Padua, Padova, Italy., Irrera O; Department of Information Engineering, University of Padua, Padova, Italy., Marchesin S; Department of Information Engineering, University of Padua, Padova, Italy., Marini N; Information Systems Institute, University of Applied Sciences Western Switzerland, Delémont, Switzerland., Müller H; Information Systems Institute, University of Applied Sciences Western Switzerland, Delémont, Switzerland., Primov T; Sirma AI, Sofia, Bulgaria.
Jazyk:	angličtina
Zdroj:	Journal of pathology informatics [J Pathol Inform] 2023 Aug 22; Vol. 14, pp. 100332. Date of Electronic Publication: 2023 Aug 22 (Print Publication: 2023).
DOI:	10.1016/j.jpi.2023.100332
Abstrakt:	Computational pathology can significantly benefit from ontologies to standardize the employed nomenclature and help with knowledge extraction processes for high-quality annotated image datasets. The end goal is to reach a shared model for digital pathology to overcome data variability and integration problems. Indeed, data annotation in such a specific domain is still an unsolved challenge and datasets cannot be steadily reused in diverse contexts due to heterogeneity issues of the adopted labels, multilingualism, and different clinical practices. Material and Methods: This paper presents the ExaMode ontology, modeling the histopathology process by considering 3 key cancer diseases (colon, cervical, and lung tumors) and celiac disease. The ExaMode ontology has been designed bottom-up in an iterative fashion with continuous feedback and validation from pathologists and clinicians. The ontology is organized into 5 semantic areas that defines an ontological template to model any disease of interest in histopathology. Results: The ExaMode ontology is currently being used as a common semantic layer in: (i) an entity linking tool for the automatic annotation of medical records; (ii) a web-based collaborative annotation tool for histopathology text reports; and (iii) a software platform for building holistic solutions integrating multimodal histopathology data. Discussion: The ontology ExaMode is a key means to store data in a graph database according to the RDF data model. The creation of an RDF dataset can help develop more accurate algorithms for image analysis, especially in the field of digital pathology. This approach allows for seamless data integration and a unified query access point, from which we can extract relevant clinical insights about the considered diseases using SPARQL queries. Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Gianmaria Silvello reports financial support was provided by 10.13039/501100000780European Commission. Filippo Fragetta is an author of the paper and a member of the editorial board of JPI. (© 2023 The Authors.)
Databáze:	MEDLINE
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