AIMC Topic: Parkinson Disease
Clear Filters Showing 31 to 40 of 535 articles
Enhancing parkinson disease detection through feature based deep learning with autoencoders and neural networks.
Scientific reports
Mar 13, 2025
Parkinson's disease is a neurodegenerative disorder that is associated with aging, leading to the progressive deterioration of certain regions of the brain. Accurate and timely diagnosis plays a crucial role in facilitating optimal therapy and improv...
The More, the Better? Evaluating the Role of EEG Preprocessing for Deep Learning Applications.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Mar 7, 2025
The last decade has witnessed a notable surge in deep learning applications for electroencephalography (EEG) data analysis, showing promising improvements over conventional statistical techniques. However, deep learning models can underperform if tra...
Item Response Modeling and Artificial Neural Network for Differentiation of Parkinson's Patients and Subjects Without Evidence of Dopaminergic Deficit.
CPT: pharmacometrics & systems pharmacology
Mar 5, 2025
Approximately 15% of patients suspected of having Parkinson's disease (PD) present dopamine active transporter (DaT) scans without evidence of dopaminergic deficits (SWEDD), most of which will never develop PD. Leveraging Movement Disorders Society U...
Deep learning models for improving Parkinson's disease management regarding disease stage, motor disability and quality of life.
Computers in biology and medicine
Mar 3, 2025
BACKGROUND AND OBJECTIVE: Motor diagnosis, monitoring and management of Parkinson's disease (PD) focuses mainly on observational methods and, clinical scales, resulting in a subjective evaluation. Inertial sensors combined with artificial intelligenc...
Deep Learning-Based Diagnostic Model for Parkinson's Disease Using Handwritten Spiral and Wave Images.
Current medical science
Mar 3, 2025
OBJECTIVE: To develop and validate a deep neural network (DNN) model for diagnosing Parkinson's Disease (PD) using handwritten spiral and wave images, and to compare its performance with various machine learning (ML) and deep learning (DL) models.
Pre-trained convolutional neural networks identify Parkinson's disease from spectrogram images of voice samples.
Scientific reports
Mar 1, 2025
Machine learning approaches including deep learning models have shown promising performance in the automatic detection of Parkinson's disease. These approaches rely on different types of data with voice recordings being the most used due to the conve...
Comprehensive clinical scale-based machine learning model for predicting subthalamic nucleus deep brain stimulation outcomes in Parkinson's disease.
Neurosurgical review
Feb 25, 2025
Parkinson's Disease (PD) is a growing burden with varied clinical manifestations and responses to Subthalamic Nucleus Deep Brain Stimulation (STN-DBS). At present, there is no effective and simple machine learning model based on comprehensive clinica...
Parkinson's disease tremor prediction towards real-time suppression: A self-attention deep temporal convolutional network approach.
Computers in biology and medicine
Feb 19, 2025
Accurate prediction of Parkinson's disease tremor (PDT) is crucial for developing assistive technologies; however, this is challenging due to the nonlinear, stochastic, and nonstationary characteristics of PDT, which substantially vary among patients...
IRMA: Machine learning-based harmonization of F-FDG PET brain scans in multi-center studies.
European journal of nuclear medicine and molecular imaging
Feb 18, 2025
PURPOSE: Center-specific effects in PET brain scans arise due to differences in technical and procedural aspects. This restricts the merging of data between centers and introduces source-specific bias.
Interpretation of basal nuclei in brain dopamine transporter scans using a deep convolutional neural network.
Nuclear medicine communications
Feb 18, 2025
OBJECTIVE: Functional imaging using the dopamine transporter (DAT) as a biomarker has proven effective in assessing dopaminergic neuron degeneration in the striatum. In assessing the neuron degeneration, visual and semiquantitative methods are used t...
Humans
Neural Networks, Computer
Female
Aged
Brain
Tomography, Emission-Computed, Single-Photon
Retrospective Studies
Convolutional Neural Networks
Dopamine Plasma Membrane Transport Proteins
Image Processing, Computer-Assisted
Parkinson Disease
Image Interpretation, Computer-Assisted
Male
Middle Aged
Deep Learning
Don't Miss the Future of Medicine
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 30978
- Neural Networks, Computer 29696
- Algorithms 26813
- Deep Learning 26324
- Artificial Intelligence 24267
- Robotics 13307
- Image Processing, Computer-Assisted 9512
- Retrospective Studies 9097
- Magnetic Resonance Imaging 6173
- Reproducibility of Results 5589
- Tomography, X-Ray Computed 4765
- Support Vector Machine 4678
- Computational Biology 4229
- Brain 4045
- Computer Simulation 3877
Recent Journals
- Scientific reports 5865
- Sensors (Basel, Switzerland) 4794
- PloS one 4011
- Neural networks : the official journal of the International Neural Network Society 2928
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1839
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1278
- IEEE journal of biomedical and health informatics 1129
- Journal of chemical information and modeling 994
- Nature communications 938
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 861
- Briefings in bioinformatics 850
- IEEE transactions on neural networks and learning systems 823