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.
Functionality and Interfaces of a Herd Health Decision Support System for Practising Dairy Cattle Veterinarians in New Zealand
