Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Parkinson's Disease: Overview01:15

Parkinson's Disease: Overview

805
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...
805
Parkinson's Disease: Treatment01:24

Parkinson's Disease: Treatment

429
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...
429
Neural Regulation01:37

Neural Regulation

40.4K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
40.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A Structured Computational Roadmap for Lipidomics in R: Reproducible Workflows from Raw Data to Functional Insight.

Metabolites·2026
Same author

Human-in-the-Loop Artificial Intelligence: A Systematic Review of Concepts, Methods, and Applications.

Entropy (Basel, Switzerland)·2026
Same author

CpGene: a web application for epigenetic signature identification from DNA methylation arrays.

Bioinformatics (Oxford, England)·2026
Same author

AI agents in Alzheimer's disease management: challenges and future directions.

Frontiers in aging neuroscience·2026
Same author

Artificial intelligence analysis of minimally invasive surgery data.

Journal of robotic surgery·2026
Same author

Web-Based Application for Hashimoto's Disease Prediction Based on Thyroid Hormone Levels and Machine Learning Analysis.

Advances in experimental medicine and biology·2026

Related Experiment Video

Updated: Oct 5, 2025

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.4K

A Sensor-Based Perspective in Early-Stage Parkinson's Disease: Current State and the Need for Machine Learning

Marios G Krokidis1, Georgios N Dimitrakopoulos1, Aristidis G Vrahatis1

  • 1Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece.

Sensors (Basel, Switzerland)
|January 22, 2022
PubMed
Summary
This summary is machine-generated.

This study explores sensor-based approaches and machine learning for diagnosing Parkinson's disease (PD). It highlights the potential of these technologies for early detection and personalized risk prediction in neurodegenerative disorders.

Keywords:
Parkinson’s diseasebiosensorsmachine learningwearable devices

More Related Videos

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

8.0K
Assessment of Sensorimotor Function in Mouse Models of Parkinson's Disease
10:32

Assessment of Sensorimotor Function in Mouse Models of Parkinson's Disease

Published on: June 17, 2013

55.6K

Related Experiment Videos

Last Updated: Oct 5, 2025

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.4K
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

8.0K
Assessment of Sensorimotor Function in Mouse Models of Parkinson's Disease
10:32

Assessment of Sensorimotor Function in Mouse Models of Parkinson's Disease

Published on: June 17, 2013

55.6K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Data Science

Background:

  • Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms, making early diagnosis challenging.
  • The loss of dopaminergic neurons and formation of Lewy bodies are key pathological features of PD.
  • Current diagnostic methods face limitations due to the insidious onset of symptoms.

Purpose of the Study:

  • To review sensor-based platforms and machine learning techniques for Parkinson's disease diagnosis.
  • To discuss the application of ensemble methods with sensor data for personalized PD risk prediction.
  • To propose a comprehensive biosensing and data processing system for PD monitoring.

Main Methods:

  • Review of current sensor-based diagnostic approaches for Parkinson's disease.
  • Examination of machine learning and ensemble techniques applied to sensor data.
  • Conceptualization of an integrated biosensing platform with clinical data processing.

Main Results:

  • Sensor-based platforms offer a promising avenue for simultaneous screening of biological signals and identification of biomarkers.
  • Machine learning integration enhances data collection, symptom classification, and supports data-driven clinical decisions for PD.
  • Ensemble techniques show potential for developing accurate, personalized risk prediction models for Parkinson's disease.

Conclusions:

  • Sensor-based technologies coupled with machine learning are crucial for advancing the early diagnosis and monitoring of Parkinson's disease.
  • An integrated biosensing system can significantly improve the management and prognosis of PD.
  • Further development in this area can lead to more effective, personalized healthcare strategies for neurodegenerative conditions.