Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Moral MedTech: Exploring Ethics-by-Design for Artificial Intelligence in Healthcare via Serious Gaming.

Studies in health technology and informatics·2026

Same author

Healthy living environments create healthy physical activity-related habits (ELEVATE): A protocol for a smartphone-based mixed-method study.

PloS one·2026

Same author

Designing effective explainable AI: a human-centered evaluation of explanation formats in financial decision-making.

Frontiers in artificial intelligence·2026

Same author

Understanding the Role of Clinical Decision Support Systems Among Hospital Nurses Using the FITT (Fit Between Individuals, Tasks, and Technology) Framework: Qualitative Study.

Journal of medical Internet research·2025

Same author

Enhancing Clinical Decision Support: A Heuristic Evaluation of Explainable AI in Healthcare Dashboards.

Studies in health technology and informatics·2025

Same author

Exploring Differential Diagnosis-Based Explainable AI: A Case Study in Melanoma Detection.

Studies in health technology and informatics·2025

Same journal

A GenAI Pipeline for Violinist Kinematic Data Management.

Studies in health technology and informatics·2026

Same journal

AMAL-For-Qatar: A Comprehensive AI Ecosystem for Fetal Ultrasound Analysis - Project Overview and Achievements.

Studies in health technology and informatics·2026

Same journal

Longitudinal Treatment-Aware Multimodal AI for Dermatology: A Scoping Review.

Studies in health technology and informatics·2026

Same journal

Predicting Postpartum Depression Using Imbalance-Aware Machine Learning.

Studies in health technology and informatics·2026

Same journal

Validation of Deep-Learning Models for Autosegmentation of Brain Metastases.

Studies in health technology and informatics·2026

Same journal

Delay-Dependent Gating in Modular RNNs.

Studies in health technology and informatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 24, 2026

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

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

Published on: July 24, 2019

Explainable Machine Learning for Parkinson's Disease Screening Using Spiral Drawing Test.

Manlu He¹, Aarnout Brombacher^1,2, Danielle Sent¹

¹Jheronimus Academy of Data Science, Eindhoven University of Technology, 's-Hertogenbosch, The Netherlands.

Studies in Health Technology and Informatics

|May 23, 2026

Summary

This summary is machine-generated.

This study introduces an explainable AI model for Parkinson's disease (PD) detection using spiral drawings. The model achieves competitive results while providing clinically relevant explanations, enhancing trust in AI for neurological diagnosis.

Keywords:

Explainable AI Machine Learning Parkinson’s Disease Screening Spiral Drawing Test

More Related Videos

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

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

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Related Experiment Videos

Last Updated: May 24, 2026

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

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

Published on: July 24, 2019

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

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

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Area of Science:

Neurology
Artificial Intelligence
Medical Imaging

Background:

Parkinson's disease (PD) significantly impacts patient quality of life.
The Archimedean spiral drawing test is a key diagnostic tool for PD.
Deep learning shows promise in PD detection from drawings but lacks clinical explainability.

Purpose of the Study:

To develop an explainable machine learning approach for PD diagnosis using spiral drawing images.
To integrate clinically relevant features into the diagnostic model.
To enhance trust and clinical alignment of AI-driven diagnostic tools.

Main Methods:

Designed an explainable machine learning model for PD detection.
Utilized clinically relevant features from spiral drawing images.
Employed advanced computer vision and deep learning techniques.

Main Results:

The model achieved competitive diagnostic performance.
Explanations revealed focus on critical diagnostic factors.
Predictions aligned with established clinical findings for PD.

Conclusions:

The explainable AI approach supports PD diagnosis effectively.
Model explanations foster clinical understanding and trust.
This method promotes the integration of AI in neurological diagnostics.