AI Medical Compendium Journal:
Parkinsonism & related disorders

Showing 1 to 10 of 22 articles

The third wheel or the game changer? How AI could team up with neurologists in Parkinson's care.

Parkinsonism & related disorders
INTRODUCTION: Parkinson's disease (PD) is a progressive neurodegenerative disorder marked by diverse motor and non-motor symptoms. AI appears to be the elephant in the neurologist's room, albeit offering transformative potential in early diagnosis, p...

Machine Learning-based World Health Organization Disability Assessment Schedule for persons with Parkinson's disease.

Parkinsonism & related disorders
INTRODUCTION: The World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) is a well-known measure to assess disability in persons with Parkinson's disease (PD). The purpose of this study was to develop a short form of the WHODAS 2.0...

Application of elastic net for clinical outcome prediction and classification in progressive supranuclear palsy: A multicenter cohort study.

Parkinsonism & related disorders
BACKGROUND: Previous studies have used machine learning to identify clinically relevant atrophic regions in progressive supranuclear palsy (PSP). This study applied Elastic Net (EN) in PSP to uncover key atrophic patterns, offering a novel approach t...

Development and validation of a machine learning-based diagnostic model for Parkinson's disease in community-dwelling populations: Evidence from the China health and retirement longitudinal study (CHARLS).

Parkinsonism & related disorders
BACKGROUND: Parkinson's disease (PD) is a major neurodegenerative disorder in Middle-aged and elderly people.There is a pressing need for effective predictive models, particularly in chinese population.

What the trained eye cannot see: Quantitative kinematics and machine learning detect movement deficits in early-stage Parkinson's disease from videos.

Parkinsonism & related disorders
BACKGROUND: Evaluation of disease severity in Parkinson's disease (PD) relies on motor symptoms quantification. However, during early-stage PD, these symptoms are subtle and difficult to quantify by experts, which might result in delayed diagnosis an...

Levodopa-induced dyskinesia in Parkinson's disease: Insights from cross-cohort prognostic analysis using machine learning.

Parkinsonism & related disorders
BACKGROUND: Prolonged levodopa treatment in Parkinson's disease (PD) often leads to motor complications, including levodopa-induced dyskinesia (LID). Despite continuous levodopa treatment, some patients do not develop LID symptoms, even in later stag...

The role of AI and machine learning in the diagnosis of Parkinson's disease and atypical parkinsonisms.

Parkinsonism & related disorders
Parkinson's disease is a neurodegenerative movement disorder associated with motor and non-motor symptoms causing severe disability as the disease progresses. The development of biomarkers for Parkinson's disease to diagnose patients earlier and pred...

Are the criteria for PD-MCI diagnosis comprehensive? A Machine Learning study with modified criteria.

Parkinsonism & related disorders
BACKGROUND: Mild cognitive impairment in Parkinson's disease (PD-MCI) includes deficits in different cognitive domains, and one domain to explore for neurocognitive impairment following the DSM-V is social cognition. However, this domain is not inclu...

Machine learning models for diagnosis of essential tremor and dystonic tremor using grey matter morphological networks.

Parkinsonism & related disorders
BACKGROUND: Essential tremor (ET) and dystonic tremor (DT) are the two most common tremor disorders, and misdiagnoses are very common due to similar tremor symptoms. In this study, we explore the structural network mechanisms of ET and DT using brain...

Combined blood Neurofilament light chain and third ventricle width to differentiate Progressive Supranuclear Palsy from Parkinson's Disease: A machine learning study.

Parkinsonism & related disorders
INTRODUCTION: Differentiating Progressive Supranuclear Palsy (PSP) from Parkinson's Disease (PD) may be clinically challenging. In this study, we explored the performance of machine learning models based on MR imaging and blood molecular biomarkers i...