Parkinson's Disease

Artificial intelligence for assisting diagnostics and assessment of Parkinson's disease-A review.

Artificial intelligence, specifically machine learning, has found numerous applications in computer-...

Towards computerized diagnosis of neurological stance disorders: data mining and machine learning of posturography and sway.

We perform classification, ranking and mapping of body sway parameters from static posturography dat...

Identifying incident dementia by applying machine learning to a very large administrative claims dataset.

Alzheimer's disease and related dementias (ADRD) are highly prevalent conditions, and prior efforts ...

Toward Safe Retinal Microsurgery: Development and Evaluation of an RNN-Based Active Interventional Control Framework.

OBJECTIVE: Robotics-assisted retinal microsurgery provides several benefits including improvement of...

Deep learning to differentiate parkinsonian disorders separately using single midsagittal MR imaging: a proof of concept study.

OBJECTIVES: To evaluate the diagnostic performance of deep learning with the convolutional neural ne...

Optimized machine learning methods for prediction of cognitive outcome in Parkinson's disease.

BACKGROUND: Given the increasing recognition of the significance of non-motor symptoms in Parkinson'...

Serum N-Glycosylation in Parkinson's Disease: A Novel Approach for Potential Alterations.

In this study, we present the application of a novel capillary electrophoresis (CE) method in combin...

Atrophy of cerebellar peduncles in essential tremor: a machine learning-based volumetric analysis.

BACKGROUND: Subtle cerebellar signs are frequently observed in essential tremor (ET) and may be asso...

Non-Linear Dynamical Analysis of Resting Tremor for Demand-Driven Deep Brain Stimulation.

Parkinson's Disease (PD) is currently the second most common neurodegenerative disease. One of the m...

Machine learning-aided personalized DTI tractographic planning for deep brain stimulation of the superolateral medial forebrain bundle using HAMLET.

BACKGROUND: Growing interest exists for superolateral medial forebrain bundle (slMFB) deep brain sti...

Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A Meta-Analysis.

A psychological disorder is a mutilation state of the body that intervenes the imperative functionin...

Use of Magnetic Resonance Imaging and Artificial Intelligence in Studies of Diagnosis of Parkinson's Disease.

Parkinson's disease (PD) is a common neurodegenerative disorder. It has a delitescent onset and a sl...

A Reservoir Computing Model of Reward-Modulated Motor Learning and Automaticity.

Reservoir computing is a biologically inspired class of learning algorithms in which the intrinsic d...

Machine-learning identifies Parkinson's disease patients based on resting-state between-network functional connectivity.

OBJECTIVE: Evaluation of a data-driven, model-based classification approach to discriminate idiopath...

Implications of asymmetric neural activity patterns in the basal ganglia outflow in the integrative neural network model for cervical dystonia.

Cervical dystonia (CD) is characterized by abnormal twisting and turning of the head with associated...

Classification of degenerative parkinsonism subtypes by support-vector-machine analysis and striatal I-FP-CIT indices.

OBJECTIVES: To provide an automated classification method for degenerative parkinsonian syndromes (P...

Development of a robotic walker for individuals with cerebral palsy.

This study describes the first use of a robotic walker in youth and young adults with cerebral pals...

Computer vision of smartphone video has potential to detect functional tremor.

Differential diagnosis of multiple system atrophy with predominant parkinsonism and Parkinson's disease using neural networks.

Differential diagnosis between Parkinson's disease (PD) and atypical parkinsonism, such as multiple ...

Feasible Classified Models for Parkinson Disease from Tc-TRODAT-1 SPECT Imaging.

The neuroimaging techniques such as dopaminergic imaging using Single Photon Emission Computed Tomog...

DeepQSM - using deep learning to solve the dipole inversion for quantitative susceptibility mapping.

Quantitative susceptibility mapping (QSM) is based on magnetic resonance imaging (MRI) phase measure...