AIMC Topic: Parkinson Disease

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Deep Learning for Daily Monitoring of Parkinson's Disease Outside the Clinic Using Wearable Sensors.

Sensors (Basel, Switzerland)
Now that wearable sensors have become more commonplace, it is possible to monitor individual healthcare-related activity outside the clinic, unleashing potential for early detection of events in diseases such as Parkinson's disease (PD). However, the...

Magnetic resonance imaging image analysis of the therapeutic effect and neuroprotective effect of deep brain stimulation in Parkinson's disease based on a deep learning algorithm.

International journal for numerical methods in biomedical engineering
In order to study the therapeutic neuroprotective effect of deep brain stimulation (DBS) in Parkinson's disease (PD), based on the deep learning algorithm, this study combines with magnetic resonance imaging (MRI) image analysis technology to study t...

Finger dexterity measured by the Grooved Pegboard test indexes Parkinson's motor severity in a tremor-independent manner.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology
Fine motor impairments are frequent complaints in people with Parkinson's disease (PD). While they may develop at an early stage of the disease, they become more problematic as the disease progresses. Tremors and fine motor symptoms may seem related,...

Effective Evaluation of Medical Images Using Artificial Intelligence Techniques.

Computational intelligence and neuroscience
This work is implemented for the management of patients with epilepsy, and methods based on electroencephalography (EEG) analysis have been proposed for the timely prediction of its occurrence. The proposed system is used for crisis detection and pre...

Deep learning based low-activity PET reconstruction of [C]PiB and [F]FE-PE2I in neurodegenerative disorders.

NeuroImage
PURPOSE: Positron Emission Tomography (PET) can support a diagnosis of neurodegenerative disorder by identifying disease-specific pathologies. Our aim was to investigate the feasibility of using activity reduction in clinical [F]FE-PE2I and [C]PiB PE...

Use of deep learning-based radiomics to differentiate Parkinson's disease patients from normal controls: a study based on [F]FDG PET imaging.

European radiology
OBJECTIVES: We proposed a novel deep learning-based radiomics (DLR) model to diagnose Parkinson's disease (PD) based on [F]fluorodeoxyglucose (FDG) PET images.

Deep learning architectures for Parkinson's disease detection by using multi-modal features.

Computers in biology and medicine
BACKGROUND: The use of multi-modal features for improving the diagnosing accuracy of Parkinson's disease (PD) is still under consideration.

Decoding the dopamine transporter imaging for the differential diagnosis of parkinsonism using deep learning.

European journal of nuclear medicine and molecular imaging
PURPOSE: This work attempts to decode the discriminative information in dopamine transporter (DAT) imaging using deep learning for the differential diagnosis of parkinsonism.

Optimization-Based Ensemble Feature Selection Algorithm and Deep Learning Classifier for Parkinson's Disease.

Journal of healthcare engineering
PD (Parkinson's Disease) is a severe malady that is painful and incurable, affecting older human beings. Identifying PD early in a precise manner is critical for the lengthened survival of patients, where DMTs (data mining techniques) and MLTs (machi...

Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts.

Nature communications
Drug discovery for diseases such as Parkinson's disease are impeded by the lack of screenable cellular phenotypes. We present an unbiased phenotypic profiling platform that combines automated cell culture, high-content imaging, Cell Painting, and dee...