Welcome to the InfoSci Platform
E-Health Decision Support Systems for the Diagnosis of Dementia Diseases

E-Health Decision Support Systems for the Diagnosis of Dementia Diseases

Isabella Castiglioni, Maria Carla Gilardi, Francesca Gallivanone
Copyright: © 2013 |Pages: 14
ISBN13: 9781466626577|ISBN10: 1466626887|EISBN13: 9781466626881
DOI: 10.4018/978-1-4666-2657-7.ch006
Cite Chapter Cite Chapter

MLA

Castiglioni, Isabella, et al. "E-Health Decision Support Systems for the Diagnosis of Dementia Diseases." E-Health Technologies and Improving Patient Safety: Exploring Organizational Factors, edited by Anastasius Moumtzoglou and Anastasia N. Kastania, IGI Global, 2013, pp. 84-97. https://doi.org/10.4018/978-1-4666-2657-7.ch006

APA

Castiglioni, I., Gilardi, M. C., & Gallivanone, F. (2013). E-Health Decision Support Systems for the Diagnosis of Dementia Diseases. In A. Moumtzoglou & A. Kastania (Eds.), E-Health Technologies and Improving Patient Safety: Exploring Organizational Factors (pp. 84-97). IGI Global. https://doi.org/10.4018/978-1-4666-2657-7.ch006

Chicago

Castiglioni, Isabella, Maria Carla Gilardi, and Francesca Gallivanone. "E-Health Decision Support Systems for the Diagnosis of Dementia Diseases." In E-Health Technologies and Improving Patient Safety: Exploring Organizational Factors, edited by Anastasius Moumtzoglou and Anastasia N. Kastania, 84-97. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2657-7.ch006

Export Reference

Mendeley
Favorite

Abstract

The increase of incidence and prevalence of dementia diseases makes urgent the clinical community to be supported in the difficult diagnostic process of dementia patients. E-health decision support systems, based on innovative algorithms able to extract information from in vivo neuroimaging studies, can make a quite different way to perform neurological diagnosis and enlarge domains and actors involved in the diagnostic process. A number of image-processing methods that extract potential biomarkers from the in vivo neuroimaging studies have been proposed (e.g. volume segmentation, voxel-based statistical mapping). A number of new shape descriptors have also been developed (e.g. texture-based). Other approaches (e.g. machine learning, pattern recognition) have been proven effective, for both structural and functional data, in making automatic diagnoses. The integration of these sophisticated diagnostic tools into secure, efficient, and wide e-infrastructures is the prerequisite for the real implementation of e-health support services to the clinical and industrial communities managing dementia patients.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.