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|Title:||An ontology-based module of the information system ScolioMedIS for 3D digital diagnosis of adolescent scoliosis|
|Journal:||Computer Methods and Programs in Biomedicine|
|Abstract:||© 2019 Elsevier B.V. Background and objective: Conventional information systems are built on top of a relational database. The main weakness of these systems is impossibility to define stable data schema ahead when the knowledge of the system is evolving and dynamic. The widely accepted alternatives to relational databases are ontologies that can be used for designing information systems. Many research papers describe various methods for improving reliability and precision in generating the type of the Lenke classification based on the image processing techniques or a computer program, but all of them require radiograph images. The main objective of this paper is to demonstrate the development of an ontology-based module of the information system ScolioMedIS for adolescent idiopathic scoliosis (AIS) diagnosis and monitoring, which uses optical 3D methods to determine the Lenke classification of AIS and to avoid harmful effects of traditional radiation diagnosis. Methods: For creating an ontology-based module of the ScolioMedIS we used the following steps: specification, conceptualization, formalization and implementation. In the specification and conceptualization phase we performed data collection and analysis to define domain, concepts and relationships for ontology design. In the formalization and implementation stage we developed the OBR-Scolio ontology and the ontology-based module of the ScolioMedIS. The module employs the Protégé-OWL API, as a collection of Java interfaces for the OBR-Scolio ontology, which enables the creating, deleting, and editing of the basic elements of the OBR-Scolio ontology, as well as the querying of the ontology. Results: The ontology-based module of ScolioMedIS is tested on the datasets of 20 female and 15 male patients with AIS between the ages of 11 and 18, to categorize spinal curvatures and to automatically generate statistical indicators about the frequency of the basic spinal curvatures, degree of progression or regression of deformity and statistical indicators about curvature characteristics according to the Lenke classification system and Lenke scoliosis types. Results are then compared with analysis of the Lenke classification of 315 observed patients, performed using traditional radiation techniques. Conclusions: This part of the system allows continuous monitoring of the progression/regression of spinal curvatures for each registered patient, which may provide a better management of scoliosis (diagnosis and treatment).|
|Appears in Collections:||Institute for Information Technologies, Kragujevac|
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