AIMC Topic: Parkinson Disease

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An automatic interpretable deep learning pipeline for accurate Parkinson's disease diagnosis using quantitative susceptibility mapping and T1-weighted images.

Human brain mapping
Parkinson's disease (PD) diagnosis based on magnetic resonance imaging (MRI) is still challenging clinically. Quantitative susceptibility maps (QSM) can potentially provide underlying pathophysiological information by detecting the iron distribution ...

Denoising approach with deep learning-based reconstruction for neuromelanin-sensitive MRI: image quality and diagnostic performance.

Japanese journal of radiology
PURPOSE: Neuromelanin-sensitive MRI (NM-MRI) has proven useful for diagnosing Parkinson's disease (PD) by showing reduced signals in the substantia nigra (SN) and locus coeruleus (LC), but requires a long scan time. The aim of this study was to asses...

Automatic detection of Parkinson's disease from power spectral density of electroencephalography (EEG) signals using deep learning model.

Physical and engineering sciences in medicine
Parkinson's disease (PD) is characterized by slowed movements, speech disorders, an inability to control muscle movements, and tremors in the hands and feet. In the early stages of PD, the changes in these motor signs are very vague, so an objective ...

Diagnosis of Parkinson's Disease via the Metabolic Fingerprint in Saliva by Deep Learning.

Small methods
Parkinson's disease (PD) is the second cause of the neurodegenerative disorder, affecting over 6 million people worldwide. The World Health Organization estimated that population aging will cause global PD prevalence to double in the coming 30 years....

Multi-Modal Deep Learning Diagnosis of Parkinson's Disease-A Systematic Review.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Parkinson's Disease (PD) is among the most frequent neurological disorders. Approaches that employ artificial intelligence and notably deep learning, have been extensively embraced with promising outcomes. This study dispenses an exhaustive review be...

msQSM: Morphology-based self-supervised deep learning for quantitative susceptibility mapping.

NeuroImage
Quantitative susceptibility mapping (QSM) has been applied to the measurement of iron deposition and the auxiliary diagnosis of neurodegenerative disease. There still exists a dipole inversion problem in QSM reconstruction. Recently, deep learning ap...

Deep learning regressor model based on nigrosome MRI in Parkinson syndrome effectively predicts striatal dopamine transporter-SPECT uptake.

Neuroradiology
PURPOSE: Nigrosome imaging using susceptibility-weighted imaging (SWI) and dopamine transporter imaging using I-2β-carbomethoxy-3β-(4-iodophenyl)-N-(3-fluoropropyl)-nortropane (I-FP-CIT) single-photon emission computerized tomography (SPECT) can eval...

Validity of artificial intelligence-based markerless motion capture system for clinical gait analysis: Spatiotemporal results in healthy adults and adults with Parkinson's disease.

Journal of biomechanics
Markerless motion capture methods are continuously in development to target limitations encountered in marker-, sensor-, or depth-based systems. Previous evaluation of the KinaTrax markerless system was limited by differences in model definitions, ga...

Stereotactic robot-assisted MRI-guided laser interstitial thermal therapy thalamotomy for medically intractable Parkinson's disease tremor: technical note and preliminary effects on 2 cases.

Acta neurochirurgica
BACKGROUND: Medically intractable Parkinson's disease (PD) tremor is a common difficult clinical situation with major impact on patient's quality of life (QOL). Deep brain stimulation (DBS) is an effective therapy but is not an option for many patien...

Deep learning segmentation results in precise delineation of the putamen in multiple system atrophy.

European radiology
OBJECTIVES: The precise segmentation of atrophic structures remains challenging in neurodegenerative diseases. We determined the performance of a Deep Neural Patchwork (DNP) in comparison to established segmentation algorithms regarding the ability t...