Predicting Improvement in Parkinson’s Disease Patients using Location of Stimulation

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Predicting Improvement in Parkinson’s Disease Patients using Location of Stimulation
Title:
Predicting Improvement in Parkinson’s Disease Patients using Location of Stimulation
Journal Title:
2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Keywords:
Publication Date:
03 December 2025
Citation:
Susanto, K., Premchand, B., Wan, K. R., Ng, W. H., Misbaah, F., Yu, W. Y., Chan, V. E. Y., Oh, H. P., & So, R. Q. (2025). Predicting Improvement in Parkinson’s Disease Patients using Location of Stimulation. 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 1–4. https://doi.org/10.1109/embc58623.2025.11253202
Abstract:
Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has become a standard therapy for Parkinson’s disease (PD). However, the capability to predict symptom improvement based on specific electrode locations has not yet been developed. In this study, we analyzed pre- and postoperative MRI/CT data and stimulation parameters from PD patients to map electrode locations and estimate the volume of electric field (VEF) around the STN. We then modeled how these features were related to improvements in PD symptoms, measured using the Unified Parkinson’s Disease Rating Scale (UPDRS-III). Our predictive models accurately predicted improvement scores for total UPDRS scores (MSE = 13.92, R2 = 0.78) and individual symptoms such as rigidity (MSE = 0.107, R2 = 0.98), tremor (MSE = 1.56, R2 = 0.85), bradykinesia (MSE = 4.67, R2 = 0.85), and freezing of gait (MSE = 0.28, R2 = 0.84). These findings support the development of models for symptom-specific targeting in DBS, with the STN associative VEF shown to be a key predictive feature, potentially improving clinical outcomes and patient quality of life.Clinical relevance—This study provides predictive models that help clinicians tailor DBS targeting to individual Parkinson’s symptom profiles based on stimulation location.
License type:
Publisher Copyright
Funding Info:
This research is supported by Singhealth Duke Academic medicine research grant (Artificial Intelligence) AM/AIR011/2022 (SRDUKAMR22A1), the Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR) Singapore, and the Singapore International Graduate Award (SINGA), A*STAR Graduate Academy (A*GA). The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The authors had full access to all the data in the study and had overall responsibility for the decision to submit for publication. No funding was received from the drug manufacturer or distributor.
Description:
© 2025 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
ISSN:
2694-0604
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