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Parkinson's Disease: Overview01:15

Parkinson's Disease: Overview

704
Neurodegenerative disorders are progressive diseases that cause irreversible damage and loss to neurons in specific brain areas. Examples of these disorders include Parkinson's disease, Alzheimer's disease, Multiple Sclerosis (MS), and Amyotrophic Lateral Sclerosis (ALS). These disorders share characteristics such as proteinopathies, selective neuronal vulnerability, and a complex interplay between genetic and environmental factors. The primary therapeutic goal for these conditions is...
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Parkinson's Disease: Treatment01:24

Parkinson's Disease: Treatment

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Neurodegenerative disorders, such as Parkinson's Disease (PD), involve the gradual and irreversible destruction of neurons in particular brain areas. These disorders exhibit standard features like proteinopathies, selective vulnerability of some neurons, and an interaction of intrinsic properties, genetics, and environmental influences in neural injury.
Parkinson's Disease is primarily a result of the loss of dopaminergic neurons in the substantia nigra pars compacta. The cornerstone of...
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Related Experiment Video

Updated: Sep 11, 2025

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking
07:26

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking

Published on: September 26, 2019

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Screening for Parkinson's disease using "computer vision".

Narongrit Kasemsap1,2, Purinat Tikkapanyo3, Panupong Wanjantuk4

  • 1Division of Neurology, Department of Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.

Plos One
|August 12, 2025
PubMed
Summary

Computer vision accurately detects Parkinson's disease (PD) by analyzing finger-tapping bradykinesia. Machine learning models identified key tapping features, offering a non-contact diagnostic alternative.

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Related Experiment Videos

Last Updated: Sep 11, 2025

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Area of Science:

  • Neurology
  • Biomedical Engineering
  • Computer Science

Background:

  • Bradykinesia is a key indicator for Parkinson's disease (PD) diagnosis.
  • Traditional finger-tapping tests rely on subjective physician assessments.
  • Computer vision presents a non-contact, objective, and cost-effective diagnostic approach.

Purpose of the Study:

  • To detect Parkinson's disease (PD) by identifying bradykinesia.
  • Utilize computer vision analysis of the finger-tapping test.
  • Apply machine learning models for automated diagnosis using both hands' data.

Main Methods:

  • Recruited 100 participants (PD patients and healthy controls).
  • Analyzed 10-second finger-tapping movements recorded via smartphone using Google MediaPipe Hands.
  • Trained six machine learning models with a nested cross-validation framework.

Main Results:

  • PD patients showed significantly greater differences in tapping scores between hands compared to controls (p=0.001).
  • Tapping amplitude variations and decremental parameters significantly differed in PD patients.
  • Machine learning models, particularly support vector machines, accurately classified PD based on tapping features.

Conclusions:

  • Computer vision effectively detects bradykinesia from finger-tapping tests.
  • This method provides an objective and accurate approach for Parkinson's disease diagnosis.
  • Simultaneous tapping analysis of both hands enhances diagnostic capability.