Exploring the Potential Imaging Biomarkers for Parkinson's Disease Using Machine Learning Approach

Illia Mushta; Sulev Koks; Anton Popov; Oleksandr Lysenko

doi:10.3390/bioengineering12010011

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Exploring the Potential Imaging Biomarkers for Parkinson's Disease Using Machine Learning Approach

Journal article

Open access

Peer reviewed

Exploring the Potential Imaging Biomarkers for Parkinson's Disease Using Machine Learning Approach

Illia Mushta, Sulev Koks, Anton Popov and Oleksandr Lysenko

Bioengineering (Basel), Vol.12(1), 11

2024

DOI: https://doi.org/10.3390/bioengineering12010011

PMID: 39851285

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Abstract

AdaBoost

basal ganglia

classification

machine learning

DATSCAN

Parkinson’s disease

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor and neuropsychiatric symptoms resulting from the loss of dopamine-producing neurons in the substantia nigra pars compacta (SNc). Dopamine transporter scan (DATSCAN), based on single-photon emission computed tomography (SPECT), is commonly used to evaluate the loss of dopaminergic neurons in the striatum. This study aims to identify a biomarker from DATSCAN images and develop a machine learning (ML) algorithm for PD diagnosis. Using 13 DATSCAN-derived parameters and patient handedness from 1309 individuals in the Parkinson's Progression Markers Initiative (PPMI) database, we trained an AdaBoost classifier, achieving an accuracy of 98.88% and an area under the receiver operating characteristic (ROC) curve of 99.81%. To ensure interpretability, we applied the local interpretable model-agnostic explainer (LIME), identifying contralateral putamen SBR as the most predictive feature for distinguishing PD from healthy controls. By focusing on a single biomarker, our approach simplifies PD diagnosis, integrates seamlessly into clinical workflows, and provides interpretable, actionable insights. Although DATSCAN has limitations in detecting early-stage PD, our study demonstrates the potential of ML to enhance diagnostic precision, contributing to improved clinical decision-making and patient outcomes.

Details

Title: Exploring the Potential Imaging Biomarkers for Parkinson's Disease Using Machine Learning Approach
Authors/Creators: Illia Mushta - National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”
Sulev Koks - Murdoch University, Centre for Molecular Medicine and Innovative Therapeutics
Anton Popov - Faculty of Applied Sciences, Ukrainian Catholic University, 79026 Lviv, Ukraine
Oleksandr Lysenko - Department of Electronic Computational Equipment Design, National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 03056 Kyiv, Ukraine
Publication Details: Bioengineering (Basel), Vol.12(1), 11
Publisher: MDPI
Number of pages: 29
Grant note: No / Lesa Melnyczuk
Identifiers: 991005737344307891
Murdoch Affiliation: Murdoch University; Personalised Medicine Centre
Language: English
Resource Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Citation topics: 1 Clinical & Life Sciences; 1.235 Throat & Voice Disorders; 1.235.1185 Voice Disorders
Web Of Science research areas: Biotechnology & Applied Microbiology; Engineering, Biomedical
ESI research areas: Engineering