Latest AI and machine learning research in parkinson's disease for healthcare professionals.
Diagnosis of Parkinson's disease (PD) remains a major clinical challenge, particularly in the prodromal and early clinically evident stages, when symptoms are subtle and phenotypically overlap with other mimicking disorders. The widespread availability of smartphones equipped with inertial, acoustic, touchscreen, and geolocation sensors has enabled the emergence of smartphone-based digital biomark...
Parkinson's disease (PD) is a progressive neurodegenerative disorder with a prolonged prodromal phase and complex motor symptoms. Despite improved clinical criteria, early diagnosis and longitudinal monitoring remain challenging. While cerebrospinal fluid (CSF) and plasma metabolites and proteins show biomarker potential, their utility in predictive models is insufficiently characterized. We emplo...
Thirty years on from the introduction of deep brain stimulation as a therapy for Parkinson's disease, adaptive deep brain stimulation (aDBS) is poised...
In deep brain stimulation (DBS) surgery for Parkinson's disease (PD), the accurate intraoperative identification of key nuclei-such as the subthalamic...
Parkinson's disease (PD) is a complex neurodegenerative disorder in which environmental toxins play a critical etiological role. Rotenone, a classical...
INTRODUCTION: Timely identification of Parkinson's disease (PD) is often delayed because of clinical heterogeneity and limited awareness of early symp...
In the 21st century, neuroglial research has entered a period of Renaissance, extending the views of prominent neuroanatomists and neurologists of the...
Oculoplasty and orbital surgeries demand high precision due to confined anatomy and nearby critical structures. Recent innovations in orbital surgerie...
Brain single-photon emission computed tomography (SPECT) imaging using I-123 DaTSCAN is an effective tool for the diagnosis and follow-up of Parkinson...
Neurodegenerative diseases, including Alzheimer's disease (AD) and Parkinson's disease (PD), are multifactorial diseases that are characterized by sev...
BACKGROUND: Deep-learning models are capable of predicting age from retinal scans and the difference between this and chronological age, retinal age g...
Encephalitic alphaviruses such as Western equine encephalitis virus (WEEV) result in significant morbidity through acute viremia and postencephalitic ...
BACKGROUND: Sleep architecture and circadian rhythms are frequently disrupted in Parkinson's disease (PD). BrainSense-enabled neurostimulators combine...
BACKGROUND: Voice-based deep learning models for Parkinson disease (PD) and dementia screening report areas under the curve (AUCs) of 0.85-0.97, but r...
G protein-coupled receptors (GPCRs) are major therapeutic targets for central nervous system disorders, with more than 500 approved drugs targeting th...
RATIONALE AND OBJECTIVES: Parkinson's disease (PD) is characterized by disrupted basal ganglia-thalamo-cortical connectivity, yet how frontal network ...
BACKGROUND: Automated multimedia analysis of remotely recorded tasks offers a scalable approach to screening and remote monitoring of movement disorde...
INTRODUCTION: Identifying deep brain stimulation (DBS) candidates, particularly those without access to an advanced specialty center, presents ongoing...
AIMS: Pathology is undergoing a paradigm shift as diagnostics become increasingly multimodal and computational. Yet legacy structures, incentives and ...
BACKGROUND: Essential tremor (ET) is the most common movement disorder in the elderly. Despite a close relationship between ET onset and age, it remai...