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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
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| Series: | Bioengineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5354/12/1/11 |
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| Summary: | 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. |
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| ISSN: | 2306-5354 |