View clinical trials related to Parkinsonian Disorders.
Filter by:The diagnosis of Parkinson's disease (PD) relies mainly on clinical observation of the patient, looking for the three characteristic symptoms and sometimes remains a real challenge. Machine Learning (ML) algorithms could help to diagnose PD early and differentiate idiopathic PD from atypical Parkinsonian syndromes. In this context, the work of Castillo-Barnes' team provided a set of imaging features based on morphological characteristics extracted from DaTSCAN® or Ioflupane (iodine-123-labeled radiopharmaceutical) single-photon emission computed tomography (SPECT) scans to discern healthy participants from participants with Parkinson's disease in a balanced set of SPECTs from the "Parkinson's Progression Markers Initiative" (PPMI) data base. The team of a study evaluated the classification performance of Parkinson's patients and normal controls when semi-quantitative indicators and shape features obtained on the dopamine transporter (DAT) by Ioflupane (123I-IP) single-photon emission computed tomography (SPECT) are combined as a machine learning (ML) feature. Artificial Intelligence (AI) based methods can improve diagnostic assessments. Several dopaminergic imaging studies using Artificial have reported accuracy of up to 90% for the diagnosis of PD. These automated approaches use machine learning methods, based on textural analyses, to (i) differentiate PD and healthy subjects, (ii) differentiate PD and vascular parkinsonism, and (iii) distinguish between different forms of atypical parkinsonism. A study conducted in 2 centers using a linear support vector machine (SVM) model discriminated patients with PD and healthy subjects with an accuracy of 82.5%.This performance is similar to visual assessment by nuclear physicians A linear SVM model based on voxel values of statistical parametric images was able to differentiate PD from vascular parkinsonism with an accuracy of 90.4%. The Nancy team has extensive experience in the detection of PD in SPECT and SPECT/CT scans with Ioflupane or DaTSCAN™
In this 12-week, open-label pilot study, the investigators will enroll 20 subjects with Parkinson's Disease to determine if two doses of Zonisamide are tolerable and demonstrate clinical benefit for Dyskinesias. The primary outcome measure is tolerability, as determined by number of subjects able to complete the study on their originally assigned dosage. Secondary outcome measures will use the Unified Dyskinesia Rating Scale (UDysRS), comprised of an Objective Section and a Historical Section, to compare baseline to 6 and 12-week measurements. Additional analysis of the effect of Zonisamide on quality of life will be measured by the Parkinson's Disease Quality of Life Questionnaire (PDQ-39).