Automatic detection of osteoporosis based on hybrid genetic swarm fuzzy classifier approaches.

OBJECTIVES: This study proposed a new automated screening system based on a hybrid genetic swarm fuzzy (GSF) classifier using digital dental panoramic radiographs to diagnose females with a low bone mineral density (BMD) or osteoporosis. METHODS: The geometrical attributes of both the mandibular cortical bone and trabecular bone were acquired using previously developed software. Designing an automated system for osteoporosis screening involved partitioning of the input attributes to generate an initial membership function (MF) and a rule set (RS), classification using a fuzzy inference system and optimization of the generated MF and RS using the genetic swarm algorithm. Fivefold cross-validation (5-FCV) was used to estimate the classification accuracy of the hybrid GSF classifier. The performance of the hybrid GSF classifier has been further compared with that of individual genetic algorithm and particle swarm optimization fuzzy classifiers. RESULTS: Proposed hybrid GSF classifier in identifying low BMD or osteoporosis at the lumbar spine and femoral neck BMD was evaluated. The sensitivity, specificity and accuracy of the hybrid GSF with optimized MF and RS in identifying females with a low BMD were 95.3%, 94.7% and 96.01%, respectively, at the lumbar spine and 99.1%, 98.4% and 98.9%, respectively, at the femoral neck BMD. The diagnostic performance of the proposed system with femoral neck BMD was 0.986 with a confidence interval of 0.942-0.998. The highest mean accuracy using 5-FCV was 97.9% with femoral neck BMD. CONCLUSIONS: The combination of high accuracy along with its interpretation ability makes this proposed automatic system using hybrid GSF classifier capable of identifying a large proportion of undetected low BMD or osteoporosis at its early stage.

Ubiquitous-severance hospital project: implementation and results.

OBJECTIVES: The purpose of this study was to review an implementation of u-Severance information system with focus on electronic hospital records (EHR) and to suggest future improvements. METHODS: Clinical Data Repository (CDR) of u-Severance involved implementing electronic medical records (EMR) as the basis of EHR and the management of individual health records. EHR were implemented with service enhancements extending to the clinical decision support system (CDSS) and expanding the knowledge base for research with a repository for clinical data and medical care information. RESULTS: The EMR system of Yonsei University Health Systems (YUHS) consists of HP integrity superdome servers using MS SQL as a database management system and MS Windows as its operating system. CONCLUSIONS: YUHS is a high-performing medical institution with regards to efficient management and customer satisfaction; however, after 5 years of implementation of u-Severance system, several limitations with regards to expandability and security have been identified.