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E-Health Decision Support Systems for the Diagnosis of Dementia Diseases
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Author(s): Isabella Castiglioni (Institute of Molecular Bioimaging and Physiology, National Research Council, Italy), Maria Carla Gilardi (Institute of Molecular Bioimaging and Physiology, National Research Council, Italy)and Francesca Gallivanone (Institute of Molecular Bioimaging and Physiology, National Research Council, Italy)
Copyright: 2013
Pages: 14
Source title:
E-Health Technologies and Improving Patient Safety: Exploring Organizational Factors
Source Author(s)/Editor(s): Anastasius Moumtzoglou (Hellenic Society for Quality & Safety in Healthcare and P. & A. Kyriakou Children's Hospital, Greece)and Anastasia N. Kastania (Athens University of Economics and Business, Greece)
DOI: 10.4018/978-1-4666-2657-7.ch006
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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.
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