AIMC Topic: Parkinson Disease
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An Artificial Intelligence Olfactory-Based Diagnostic Model for Parkinson's Disease Using Volatile Organic Compounds from Ear Canal Secretions.
Analytical chemistry
May 28, 2025
Parkinson's Disease (PD), a frequently diagnosed neurodegenerative condition, poses a major global challenge. Early diagnosis and intervention are crucial for PD treatment. This study proposes a diagnostic model for PD that analyzes volatile organic ...
Personalized prediction of gait freezing using dynamic mode decomposition.
Scientific reports
May 28, 2025
Freezing of gait (FoG) is a common severe gait disorder in patients with advanced Parkinson's disease. The ability to predict the onset of FoG episodes early on allows for timely intervention, which is essential for improving the life quality of pati...
Automatic identification of Parkinsonism using clinical multi-contrast brain MRI: a large self-supervised vision foundation model strategy.
EBioMedicine
May 27, 2025
BACKGROUND: Valid non-invasive biomarkers for Parkinson's disease (PD) and Parkinson-plus syndrome (PPS) are urgently needed. Based on our recent self-supervised vision foundation model the Shift Window UNET TRansformer (Swin UNETR), which uses clini...
QSPR analysis of physico-chemical and pharmacological properties of medications for Parkinson's treatment utilizing neighborhood degree-based topological descriptors.
Scientific reports
May 15, 2025
Topological indices are invariant quantitative metrics associated with a molecular graph, which characterize the bonding topology of a molecule. The main aim of analyzing topological indices is to summarize and transform chemical structural informati...
Machine learning-based meta-analysis reveals gut microbiome alterations associated with Parkinson's disease.
Nature communications
May 7, 2025
There is strong interest in using the gut microbiome for Parkinson's disease (PD) diagnosis and treatment. However, a consensus on PD-associated microbiome features and a multi-study assessment of their diagnostic value is lacking. Here, we present a...
Enhancing Neurodegenerative Disease Diagnosis Through Confidence-Driven Dynamic Spatio-Temporal Convolutional Network.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
May 7, 2025
Dynamic brain networks are more effective than static networks in characterizing the evolving patterns of brain functional connectivity, making them a more promising tool for diagnosing neurodegenerative diseases. However, existing classification met...
Deep Learning-Based Algorithm for Automatic Quantification of Nigrosome-1 and Parkinsonism Classification Using Susceptibility Map-Weighted MRI.
AJNR. American journal of neuroradiology
May 2, 2025
BACKGROUND AND PURPOSE: The diagnostic performance of deep learning model that simultaneously detecting and quantifying nigrosome-1 abnormality by using susceptibility map-weighted imaging (SMwI) remains unexplored. This study aimed to develop and va...
A swin transformer and CNN fusion framework for accurate Parkinson disease classification in MRI.
Scientific reports
Apr 29, 2025
Parkinson's disease ranks as the second most prevalent neurological disorder after Alzheimer's disease. Convolutional neural networks (CNNs) have been extensively employed in Parkinson's disease (PD) detection using MR images. However, CNN models gen...
Accurate prediction of absolute prokaryotic abundance from DNA concentration.
Cell reports methods
Apr 28, 2025
Quantification of the absolute microbial abundance in a human stool sample is crucial for a comprehensive understanding of the microbial ecosystem, but this information is lost upon metagenomic sequencing. While several methods exist to measure absol...
Machine Learning-Based Diagnostic Prediction Model Using T1-Weighted Striatal Magnetic Resonance Imaging for Early-Stage Parkinson's Disease Detection.
Academic radiology
Apr 18, 2025
RATIONALE AND OBJECTIVES: Diagnosing Parkinson's disease (PD) typically relies on clinical evaluations, often detecting it in advanced stages. Recently, artificial intelligence has increasingly been applied to imaging for neurodegenerative disorders....
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