Parkinson's Disease

Improving reliability of movement assessment in Parkinson's disease using computer vision-based automated severity estimation.

BackgroundClinical assessments of motor symptoms rely on observations and subjective judgments against standardized scales, leading to variability due to confounders. Improving inter-rater agreement is essential for effective disease management.ObjectiveWe developed an objective rating system for Parkinson's disease (PD) that integrates computer vision (CV) and machine learning to correct potentia...

Review on computational methods for the detection and classification of Parkinson's Disease.

BACKGROUND AND OBJECTIVE: The worldwide estimates reveal two-fold increase in incidence of Parkinson's disease (PD) over 25 years. The two-fold increased incidence and lack of proper treatment uplifted a compelling solicitude, nagging towards accurate diagnosis. The present study aims at systematic survey on recent methodologies to light up the panorama of PD through various imaging modalities.

Knowledge Distillation Guided Interpretable Brain Subgraph Neural Networks for Brain Disorder Exploration.

The human brain is a highly complex neurological system that has been the subject of continuous exploration by scientists. With the help of modern neu...

Multi-source sparse broad transfer learning for parkinson's disease diagnosis via speech.

Diagnosing Parkinson's disease (PD) via speech is crucial for its non-invasive and convenient data collection. However, the small sample size of PD sp...

Machine Learning-based World Health Organization Disability Assessment Schedule for persons with Parkinson's disease.

INTRODUCTION: The World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) is a well-known measure to assess disability in persons wi...

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

Neural networks (NNs) possess the capability to learn complex data relationships, recognize inherent patterns by emulating human brain functions, and ...

Significance of gender, brain region and EEG band complexity analysis for Parkinson's disease classification using recurrence plots and machine learning algorithms.

Parkinson Disease (PD) is a complex neurological disorder attributed by loss of neurons generating dopamine in the SN per compacta. Electroencephalogr...

Efficient recognition of Parkinson's disease mice on stepping characters with CNN.

Parkinson's disease (PD), as the second most prevalent neurodegenerative disorder worldwide, impacts the quality of life for over 12 million patients....

PIDGN: An explainable multimodal deep learning framework for early prediction of Parkinson's disease.

BACKGROUND: Parkinson's disease (PD), the second most common neurodegenerative disease in the world, is usually not diagnosed until the later stages o...

Automated analysis of spoken language differentiates multiple system atrophy from Parkinson's disease.

BACKGROUND AND OBJECTIVES: Patients with synucleinopathies such as multiple system atrophy (MSA) and Parkinson's disease (PD) frequently display speec...

Exploring Dance as a Therapeutic Approach for Parkinson Disease Through the Social Robotics for Active and Healthy Ageing (SI-Robotics): Results From a Technical Feasibility Study.

BACKGROUND: Parkinson disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms. Recently, dance has started to be cons...

PADS-Net: GAN-based radiomics using multi-task network of denoising and segmentation for ultrasonic diagnosis of Parkinson disease.

Parkinson disease (PD) is a prevalent neurodegenerative disorder, and its accurate diagnosis is crucial for timely intervention. We propose the PArkin...

Deep learning-based differential gut flora for prediction of Parkinson's.

BACKGROUND: There had been extensive research on the role of the gut microbiota in human health and disease. Increasing evidence suggested that the gu...

DDEvENet: Evidence-based ensemble learning for uncertainty-aware brain parcellation using diffusion MRI.

In this study, we developed an Evidential Ensemble Neural Network based on Deep learning and Diffusion MRI, namely DDEvENet, for anatomical brain parc...

Real-time isolation of physiological tremor using recursive singular spectrum analysis and random vector functional link for surgical robotics.

Hand-held robotic instruments enhance precision in microsurgery by mitigating physiological tremor in real time. Current tremor filtering algorithms i...

Drug repositioning for Parkinson's disease: An emphasis on artificial intelligence approaches.

Parkinson's disease (PD) is one of the most incapacitating neurodegenerative diseases (NDDs). PD is the second most common NDD worldwide which affects...

Machine learning for early detection and severity classification in people with Parkinson's disease.

Early detection of Parkinson's disease (PD) and accurate assessment of disease progression are critical for optimizing treatment and rehabilitation. H...

Selective diagnostics of Amyotrophic Lateral Sclerosis, Alzheimer's and Parkinson's Diseases with machine learning and miRNA.

The diagnosis of neurological diseases can be expensive, invasive, and inaccurate, as it is often difficult to distinguish between different types of ...

Gait-based Parkinson's disease diagnosis and severity classification using force sensors and machine learning.

A dual-stage model for classifying Parkinson's disease severity, through a detailed analysis of Gait signals using force sensors and machine learning ...

Identifying the NEAT1/miR-26b-5p/S100A2 axis as a regulator in Parkinson's disease based on the ferroptosis-related genes.

OBJECTIVES: Parkinson's disease (PD) is a complex neurodegenerative disease with unclear pathogenesis. Some recent studies have shown that there is a ...