AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Parkinson Disease

Showing 91 to 100 of 503 articles

Clear Filters

A Parkinson's Auxiliary Diagnosis Algorithm Based on a Hyperparameter Optimization Method of Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Parkinson's disease is a common mental disease in the world, especially in the middle-aged and elderly groups. Today, clinical diagnosis is the main diagnostic method of Parkinson's disease, but the diagnosis results are not ideal, especially in the ...

Deep Factor Learning for Accurate Brain Neuroimaging Data Analysis on Discrimination for Structural MRI and Functional MRI.

IEEE/ACM transactions on computational biology and bioinformatics
Analysis of neuroimaging data (e.g., Magnetic Resonance Imaging, structural and functional MRI) plays an important role in monitoring brain dynamics and probing brain structures. Neuroimaging data are multi-featured and non-linear by nature, and it i...

Contrastive machine learning reveals Parkinson's disease specific features associated with disease severity and progression.

Communications biology
Parkinson's disease (PD) exhibits heterogeneity in terms of symptoms and prognosis, likely due to diverse neuroanatomical alterations. This study employs a contrastive deep learning approach to analyze Magnetic Resonance Imaging (MRI) data from 932 P...

SR-TWAS: leveraging multiple reference panels to improve transcriptome-wide association study power by ensemble machine learning.

Nature communications
Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for transcriptome-wide association studies (TWAS). To leverage expression imputa...

Simplification of Mobility Tests and Data Processing to Increase Applicability of Wearable Sensors as Diagnostic Tools for Parkinson's Disease.

Sensors (Basel, Switzerland)
Quantitative mobility analysis using wearable sensors, while promising as a diagnostic tool for Parkinson's disease (PD), is not commonly applied in clinical settings. Major obstacles include uncertainty regarding the best protocol for instrumented m...

Partial coherence enhances parallelized photonic computing.

Nature
Advancements in optical coherence control have unlocked many cutting-edge applications, including long-haul communication, light detection and ranging (LiDAR) and optical coherence tomography. Prevailing wisdom suggests that using more coherent light...

Attention-enhanced dilated convolution for Parkinson's disease detection using transcranial sonography.

Biomedical engineering online
BACKGROUND: Transcranial sonography (TCS) plays a crucial role in diagnosing Parkinson's disease. However, the intricate nature of TCS pathological features, the lack of consistent diagnostic criteria, and the dependence on physicians' expertise can ...

Automatic Detection and Assessment of Freezing of Gait Manifestations.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Freezing of gait (FOG) is an episodic and highly disabling symptom of Parkinson's disease (PD). Although described as a single phenomenon, FOG is heterogeneous and can express as different manifestations, such as trembling in place or complete akines...

Using Video Technology and AI within Parkinson's Disease Free-Living Fall Risk Assessment.

Sensors (Basel, Switzerland)
Falls are a major concern for people with Parkinson's disease (PwPD), but accurately assessing real-world fall risk beyond the clinic is challenging. Contemporary technologies could enable the capture of objective and high-resolution data to better i...

Spiking Laguerre Volterra networks-predicting neuronal activity from local field potentials.

Journal of neural engineering
Understanding the generative mechanism between local field potentials (LFP) and neuronal spiking activity is a crucial step for understanding information processing in the brain. Up to now, most approaches have relied on simply quantifying the coupli...