| Autor: |
Marwa M. Eid, Seelammal Chinnaperumal, Sekar Kidambi Raju, Subhash Kannan, Amal H. Alharbi, Sivaramakrishnan Natarajan, Doaa Sami Khafaga, Sayed M. Tawfeek |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
AIP Advances, Vol 14, Iss 3, Pp 035040-035040-19 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2158-3226 |
| DOI: |
10.1063/5.0194094 |
| Popis: |
Lead-based deep brain stimulation (DBS) electrodes have been employed to treat Parkinson’s disease (PD), but their limitations have led to the development of lead-free piezoelectric nanoparticle-based DBS (LF-PND-DBS). This novel approach utilizes non-invasive biocompatible piezoelectric nanoparticles to generate electrical stimulation, offering a promising alternative to traditional DBS. In this study, an innovative machine learning (ML)-optimized LF-PND-DBS system for diagnosing and evaluating PD is proposed. By leveraging ML algorithms, the optimized design of LF-PND electrodes and stimulation parameters is derived, ensuring precise and personalized treatment delivery. The ML-optimized LF-PND-DBS system was evaluated in a cohort of PD patients, demonstrating an exceptional diagnostic accuracy with a sensitivity of 99.1% and a specificity of 98.2%. It effectively assessed PD severity and response to DBS treatment, providing valuable guidance for treatment monitoring. The findings highlight the immense potential of the ML-optimized LF-PND-DBS system as a transformative tool for PD diagnosis and evaluation. This novel approach has the potential to enhance DBS efficacy, safety, and personalization, paving the way for improved patient outcomes and quality of life. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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