Parkinson's Disease

Updates in Urologic Robot Assisted Surgery.

Modern robotics is an advanced minimally invasive technology with the advantages of wristed capability, three-dimensional optics, and tremor filtration compared with conventional laparoscopy. Urologists have been early adopters of robotic surgical technology: robotics have been used in urologic oncology for more than 20 years and there has been an increasing trend for utilization in benign urologi...

Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease.

Parkinson's Disease (PD) is one of the most prevalent neurodegenerative diseases that affects tens of millions of Americans. PD is highly progressive and heterogeneous. Quite a few studies have been conducted in recent years on predictive or disease progression modeling of PD using clinical and biomarkers data. Neuroimaging, as another important information source for neurodegenerative disease, ha...

Machine learning studies on major brain diseases: 5-year trends of 2014-2018.

In the recent 5 years (2014-2018), there has been growing interest in the use of machine learning (ML) techniques to explore image diagnosis and progn...

Versatility of fuzzy logic in chronic diseases: A review.

The review aims at providing current state of evidence in the field of medicine with fuzzy logic for diagnosing diseases. Literature reveals that fuzz...

Wearable sensors for Parkinson's disease: which data are worth collecting for training symptom detection models.

Machine learning algorithms that use data streams captured from soft wearable sensors have the potential to automatically detect PD symptoms and infor...

A machine-learning approach to volitional control of a closed-loop deep brain stimulation system.

OBJECTIVE: Deep brain stimulation (DBS) is a well-established treatment for essential tremor, but may not be an optimal therapy, as it is always on, r...

Multi-Source Ensemble Learning for the Remote Prediction of Parkinson's Disease in the Presence of Source-Wise Missing Data.

As the collection of mobile health data becomes pervasive, missing data can make large portions of datasets inaccessible for analysis. Missing data ha...

Automatic localization of the subthalamic nucleus on patient-specific clinical MRI by incorporating 7 T MRI and machine learning: Application in deep brain stimulation.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has shown clinical potential for relieving the motor symptoms of advanced Parkinson's di...

Self-selected speed gait training in Parkinson's disease: robot-assisted gait training with virtual reality versus gait training on the ground.

BACKGROUND: Parkinson's disease (PD) is a neurodegenerative disorder causing progressive gait disability. Although robot-assisted gait training (RAGT)...

Electroencephalography-based machine learning for cognitive profiling in Parkinson's disease: Preliminary results.

BACKGROUND: Cognitive symptoms are common in patients with Parkinson's disease. Characterization of a patient's cognitive profile is an essential step...

Roles for globus pallidus externa revealed in a computational model of action selection in the basal ganglia.

The basal ganglia are considered vital to action selection - a hypothesis supported by several biologically plausible computational models. Of the sev...

A Validation Study of Freezing of Gait (FoG) Detection and Machine-Learning-Based FoG Prediction Using Estimated Gait Characteristics with a Wearable Accelerometer.

One of the most common symptoms observed among most of the Parkinson's disease patients that affects movement pattern and is also related to the risk ...

A review on microelectrode recording selection of features for machine learning in deep brain stimulation surgery for Parkinson's disease.

OBJECTIVE: This study seeks to systematically review the selection of features and algorithms for machine learning and automation in deep brain stimul...

The Combined Use of Transcranial Direct Current Stimulation and Robotic Therapy for the Upper Limb.

Neurologic disorders such as stroke and cerebral palsy are leading causes of long-term disability and can lead to severe incapacity and restriction of...

Implementation of deep neural networks to count dopamine neurons in substantia nigra.

Unbiased estimates of neuron numbers within substantia nigra are crucial for experimental Parkinson's disease models and gene-function studies. Unbias...

Machine learning identified an Alzheimer's disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson's disease dementia.

Utilizing the publicly available neuroimaging database enabled by Alzheimer's disease Neuroimaging Initiative (ADNI; http://adni.loni.usc.edu/ ), we h...

Parkinson's Disease Diagnosis via Joint Learning From Multiple Modalities and Relations.

Parkinson's disease (PD) is a neurodegenerative progressive disease that mainly affects the motor systems of patients. To slow this disease deteriorat...

A deep convolutional neural network approach for astrocyte detection.

Astrocytes are involved in various brain pathologies including trauma, stroke, neurodegenerative disorders such as Alzheimer's and Parkinson's disease...

Bio-inspired upper limb soft exoskeleton to reduce stroke-induced complications.

Stroke has become the leading cause of disability and the second-leading cause of mortality worldwide. Dyskinesia complications are the major reason o...

Multimodal Assessment of Parkinson's Disease: A Deep Learning Approach.

Parkinson's disease is a neurodegenerative disorder characterized by a variety of motor symptoms. Particularly, difficulties to start/stop movements h...