RadiO: a prototype application ontology for radiology reporting tasks.

We introduce RadiO, a prototype application ontology for the support of electronic radiology reporting. This application ontology is implemented in Protégé and comprises three layers: 1. a radiology report layer, capturing observations made on patient examinations through the use of a controlled vocabulary of the radiographic imaging domain (RadLex), 2.

Indexing thoracic CT reports using a preliminary version of a standardized radiological lexicon (RadLex).

Introduction: To validate a preliminary version of a radiological lexicon (RadLex) against terms found in thoracic CT reports and to index report content in RadLex term categories.

Material And Methods: Terms from a random sample of 200 thoracic CT reports were extracted using a text processor and matched against RadLex. Report content was manually indexed by two radiologists in consensus in term categories of Anatomic Location, Finding, Modifier, Relationship, Image Quality, and Uncertainty.

Four ontological models for radiological diagnostics.

In this paper we isolate four diagnostic models in radiology and define a set of diagnostic relations corresponding to each clinical situation. To achieve this, we describe a set of general formal ontological notions, as well as the ontological model of the imaging domain we employed in our analysis. On the basis of our results, we conclude that these diagnostic models and the relations contained therein could be applied to diagnostic situations outside of radiology as well.

The epistemological-ontological divide in clinical radiology.

Medical ontologies like GALEN, the FMA or SNOMED represent a kind of "100% certain" medical knowledge which is not inherent to all medical sub-domains. Clinical radiology uses computerized imaging techniques to make the human body visible and interprets the imaging findings in a clinical context delivering a textual report. For clinical radiology few standardized vocabularies are available.

An ontology for carcinoma classification for clinical bioinformatics.

There are a plenty of existing classifications and staging schemes for carcinomas, one of the most frequently used being the TNM classification. Such classifications involve entities which exist at various anatomical levels of granularity and in order to apply such classifications to the Electronic Health Care Records, one needs to build ontologies which are not only based on the formal principles but also take into consideration the diversity of the domains which are involved in clinical bioinformatics. Here we outline a formal theory for addressing these issues in a way that inferences drawn upon the ontologies would be helpful in interpreting and inferring on the entities which exist at different anatomical levels of granularity.