AIMC Topic: Parkinson Disease
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Sex-Specific Imaging Biomarkers for Parkinson's Disease Diagnosis: A Machine Learning Analysis.
Journal of imaging informatics in medicine
Sep 10, 2024
This study aimed to identify sex-specific imaging biomarkers for Parkinson's disease (PD) based on multiple MRI morphological features by using machine learning methods. Participants were categorized into female and male subgroups, and various struct...
Aging-related biomarkers for the diagnosis of Parkinson's disease based on bioinformatics analysis and machine learning.
Aging
Sep 10, 2024
Parkinson's disease (PD) is a multifactorial disease that lacks reliable biomarkers for its diagnosis. It is now clear that aging is the greatest risk factor for developing PD. Therefore, it is necessary to identify novel biomarkers associated with a...
An efficient ranking-based ensembled multiclassifier for neurodegenerative diseases classification using deep learning.
Journal of neural transmission (Vienna, Austria : 1996)
Sep 9, 2024
Neurodegenerative diseases are group of debilitating and progressive disorders that primarily affect the structure and functions of nervous system, leading to gradual loss of neurons and subsequent decline in cognitive, and behavioral activities. The...
Enhancing early Parkinson's disease detection through multimodal deep learning and explainable AI: insights from the PPMI database.
Scientific reports
Sep 9, 2024
Parkinson's is the second most common neurodegenerative disease, affecting nearly 8.5M people and steadily increasing. In this research, Multimodal Deep Learning is investigated for the Prodromal stage detection of Parkinson's Disease (PD), combining...
Houston, We Have AI Problem! Quality Issues with Neuroimaging-Based Artificial Intelligence in Parkinson's Disease: A Systematic Review.
Movement disorders : official journal of the Movement Disorder Society
Sep 5, 2024
In recent years, many neuroimaging studies have applied artificial intelligence (AI) to facilitate existing challenges in Parkinson's disease (PD) diagnosis, prognosis, and intervention. The aim of this systematic review was to provide an overview of...
Higher Order Polynomial Transformer for Fine-Grained Freezing of Gait Detection.
IEEE transactions on neural networks and learning systems
Sep 3, 2024
Freezing of Gait (FoG) is a common symptom of Parkinson's disease (PD), manifesting as a brief, episodic absence, or marked reduction in walking, despite a patient's intention to move. Clinical assessment of FoG events from manual observations by exp...
Auxiliary diagnostic method of Parkinson's disease based on eye movement analysis in a virtual reality environment.
Neuroscience letters
Sep 2, 2024
Eye movement dysfunction is one of the non-motor symptoms of Parkinson's disease (PD). An accurate analysis method for eye movement is an effective way to gain a deeper understanding of the nervous system function of PD patients. However, currently, ...
Automated Sleep Detection in Movement Disorders Using Deep Brain Stimulation and Machine Learning.
Movement disorders : official journal of the Movement Disorder Society
Aug 23, 2024
BACKGROUND: Automated sleep detection in movement disorders may allow monitoring sleep, potentially guiding adaptive deep brain stimulation (DBS).
Metabolomics Unveils Disrupted Pathways in Parkinson's Disease: Toward Biomarker-Based Diagnosis.
ACS chemical neuroscience
Aug 23, 2024
Parkinson's disease (PD) is a neurodegenerative disorder characterized by diverse symptoms, where accurate diagnosis remains challenging. Traditional clinical observation methods often result in misdiagnosis, highlighting the need for biomarker-based...
Deep Learning and fMRI-Based Pipeline for Optimization of Deep Brain Stimulation During Parkinson's Disease Treatment: Toward Rapid Semi-Automated Stimulation Optimization.
IEEE journal of translational engineering in health and medicine
Aug 22, 2024
OBJECTIVE: Optimized deep brain stimulation (DBS) is fast becoming a therapy of choice for the treatment of Parkinson's disease (PD). However, the post-operative optimization (aimed at maximizing patient clinical benefits and minimizing adverse effec...
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