Exploring the Potential Imaging Biomarkers for Parkinson’s Disease Using Machine Learning Approach
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 (S...
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MDPI AG
2024-12-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/12/1/11 |
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| author | Illia Mushta Sulev Koks Anton Popov Oleksandr Lysenko |
| author_facet | Illia Mushta Sulev Koks Anton Popov Oleksandr Lysenko |
| author_sort | Illia Mushta |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bc607384b58443e782f586e6ebc3723f |
| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-bc607384b58443e782f586e6ebc3723f2025-01-24T13:22:57ZengMDPI AGBioengineering2306-53542024-12-011211110.3390/bioengineering12010011Exploring the Potential Imaging Biomarkers for Parkinson’s Disease Using Machine Learning ApproachIllia Mushta0Sulev Koks1Anton Popov2Oleksandr Lysenko3Department of Electronic Computational Equipment Design, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, 03056 Kyiv, UkrainePerron Institute for Neurological and Translational Science, Murdoch University, Nedlands, WA 6009, AustraliaDepartment of Electronic Engineering, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, 03056 Kyiv, UkraineDepartment of Electronic Computational Equipment Design, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, 03056 Kyiv, UkraineParkinson’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.https://www.mdpi.com/2306-5354/12/1/11Parkinson’s diseaseDATSCANmachine learningbasal gangliaclassificationAdaBoost |
| spellingShingle | Illia Mushta Sulev Koks Anton Popov Oleksandr Lysenko Exploring the Potential Imaging Biomarkers for Parkinson’s Disease Using Machine Learning Approach Bioengineering Parkinson’s disease DATSCAN machine learning basal ganglia classification AdaBoost |
| title | Exploring the Potential Imaging Biomarkers for Parkinson’s Disease Using Machine Learning Approach |
| title_full | Exploring the Potential Imaging Biomarkers for Parkinson’s Disease Using Machine Learning Approach |
| title_fullStr | Exploring the Potential Imaging Biomarkers for Parkinson’s Disease Using Machine Learning Approach |
| title_full_unstemmed | Exploring the Potential Imaging Biomarkers for Parkinson’s Disease Using Machine Learning Approach |
| title_short | Exploring the Potential Imaging Biomarkers for Parkinson’s Disease Using Machine Learning Approach |
| title_sort | exploring the potential imaging biomarkers for parkinson s disease using machine learning approach |
| topic | Parkinson’s disease DATSCAN machine learning basal ganglia classification AdaBoost |
| url | https://www.mdpi.com/2306-5354/12/1/11 |
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