Ontology and tool development for documenting intraoperative monitoring in neurosurgery

Abstract

Abstract Background: Intraoperative neurophysiological monitoring (IOM) is intended to serve as an early warning system. By measuring evoked potentials during neurosurgical procedures IOM aims to determine when tumor tissue removal must be stopped to avoid damage to important structures such as the corticospinal tract. The central objective of this work is to provide an ontology that improves interrelated documentation in terms of adequate event characterizations. In contrast to a taxonomy (or meronomy), an ontology enables semantic enrichments of documentation events by modelling relationships beyond is-a or part-of, e.g., causally-related-to or occurs-in. This enhances documentation accuracy as well as the potential of useful inferences. Given our focus on extensibility and the desire to reuse existing ontologies as much as possible, we decided to use the basic formal ontology (BFO). Results: Our work has three central results: (i) an BFO-based ontology for IOM that is extended to a knowledge base, (ii) a software tool with a graphical user interface that goes beyond Protégé to involve the experts of the IOM subject field, (iii) and an evaluation of the tool in real-world documentation scenarios that allowed us to derive necessary adaptations for its productive use. The core entities of the ontology are measurements with the components timestamp, measurement type, measure values, and measurement location. We did not use the ontology of adverse events because its reliance on describing events solely as occurrents was not fully compatible with our use case of representing the documentation of those events. One crucial insight is: Occurrents such as processes are generally designed to track general dynamics, rather than to identify and document concrete processes related to individuals. Several ontologies were embedded into our ontology, e.g., the Foundation Model of Anatomy (FMA), the Human Phenotype Ontology (HPO) and the ontology for surgical process models (OntoSPM) related to general surgical terms. Our software tool was developed based on JavaFX for the frontend and Apache Jena for the backend. In the evaluation, all participants agreed that the interface could be used without having extensive technical skills. Conclusions: Basing our ontology development on BFO facilitated the start of the ontology development. It also simplifies integration of other ontologies. For example, it was highly helpful to be able to integrate parts of domain-specific BFO-based ontologies such as OntoSPM. By creating a knowledge base for IOM, investigations on event-outcome associations, e.g., “a signal change pattern X before an event Y is causally related to the outcome Z” are enabled on a semantically enriched data base.