Three major projects launched during 2016, with the collaboration of several researchers globally, concerning the Alzheimer’s Disease (AD), the Autism Spectrum Disorders (ASD), and the application of Point cloud Techniques in Endoscopic Visualization.
In the first case, a new method was designed for the early diagnosis of AD based on a complex Bayesian model and the current classification of the disease. In the second case, a new software was presented for a digitalized Art Therapy and statistical analysis in EEG measurements of patients with ASD. Finally, the R&D department implemented a new set of algorithms for the application of the PCL in endoscopic products, offering 3D visibility and pattern recognition in biomedical applications.
ALZHEIMER’S DISEASE DIAGNOSTIC MODEL
The real etiology of Alzheimer’s disease (AD) is still unclear while several risk factors have been recognized to catalytically affect the early onset and the progression of the disease. According to latest studies, AD can be categorized according to risk factors, symptoms, and pathophysiological lesions into 8 different categories. Furthermore, these 8 categories can be analyzed in depth, by adding potential biomarkers in each category which have been proved to affect the severity of the disease.
How it works
The proposed Bayesian Network has been designed according to the latest ‘Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS-ADRDA criteria’ and the model exports for every AD category the maximum probability value given the biomarkers evidence. The proposed statistical model is multi-parametric, relating several heterogeneous data like plasma and CSF tests, behavioral or imaging tests as categorical variables through prior categorical distributions.
The inclusion of NASESE software in a therapeutic session involves a neurologist, a therapist and the patient who continuously exchange data within networking electronic devices and an EEG portable device