Latest AI and machine learning research in parkinson's disease for healthcare professionals.
Genome-wide association studies of physical activity traits have mapped numerous loci, yet the molecular mechanisms through which exercise influences human biology remain poorly defined. Mechanistic progress has been limited by heritability-dominated signals, siloed single-omic analyses, and the lack of integrative models that connect genetic associations to causal, system-level pathways. We intro...
Mitochondrial and lysosomal dysfunction are central features of Parkinson's disease (PD) across major genetic forms including PRKN, SNCA, and LRRK2. We applied cell morphomics, a machine-learning-based framework combining high-content imaging with quantitative feature extraction, to analyse mitochondrial and lysosomal morphology at single-cell resolution in iPS cell-derived cortical neurons from P...
Background Genome-wide association studies (GWAS) have identified numerous risk loci for Parkinson's disease, yet identifying causal genes and mechani...
Freezing of gait (FoG) prediction is clinically meaningful only when warnings arrive sufficiently early for subsequent action. Therefore, we adopt a F...
Parkinson's disease disproportionately affects males; however, the cause of this sex difference is unknown. We found that expressing mutant -synuclein...
Deep Brain Stimulation (DBS) has proven to be a promising treatment of Parkinson's Disease (PD). DBS involves stimulating specific regions of the brai...
Parkinson's disease (PD) and Alzheimer's disease (AD) are the two most prevalent and incurable neurodegenerative diseases (NDs) worldwide, for which e...
Primary ciliary dyskinesia (PCD) belongs to the group of rare genetic disorders that is extremely hard to diagnose and treat. Current diagnostic modal...
Parkinson's disease is a neurodegenerative condition that affects the brain's neurons, and causes malfunction of nerve cells and their death. A neurot...
Deep brain stimulation (DBS) is an established intervention for Parkinson's disease (PD), but conventional open-loop systems lack adaptability, are ...
Generative models have achieved significant progress in advancing 2D image editing, demonstrating exceptional precision and realism. However, they o...
Inconsistencies between clinical and omics data may arise within medical cohorts. The identification, annotation and explanation of anomalous omics-...
Parkinson's Disease (PD) affects over 10 million people globally, with speech impairments often preceding motor symptoms by years, making speech a v...
This work presents a comprehensive theory of consciousness grounded in mathematical formalism and supported by clinical data analysis. The framework...
Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms, including cognitive impairment. Its diagnosis,...
Intelligent control of Unmanned Aerial Vehicles (UAVs) swarms has emerged as a critical research focus, and it typically requires the swarm to navig...
This study leverages an Inertial Measurement Unit (IMU) dataset to develop explainable AI methods for the early detection and prediction of Freezing...
In recent years, machine learning and deep learning have shown potential for improving Parkinson's disease (PD) diagnosis, one of the most common neur...
Pathological tremor significantly impairs daily activities and quality of life, particularly in conditions such as essential tremor and Parkinson's di...
Electroencephalography (EEG) microstate analysis is a promising technique for detecting transient brain dynamics and identifying disease-specific biom...