AI-Driven Advances in Parkinson’s Disease Neurosurgery: Enhancing Patient Selection, Trial Efficiency, and Therapeutic Outcomes
Parkinson’s disease (PD) is a progressive neurodegenerative disorder marked by motor and non-motor dysfunctions that severely compromise patients’ quality of life. While pharmacological treatments provide symptomatic relief in the early stages, advanced PD often requires neurosurgical interventions,...
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MDPI AG
2025-05-01
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| Series: | Brain Sciences |
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| Online Access: | https://www.mdpi.com/2076-3425/15/5/494 |
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| author | José E. Valerio Guillermo de Jesús Aguirre Vera Maria P. Fernandez Gomez Jorge Zumaeta Andrés M. Alvarez-Pinzon |
| author_facet | José E. Valerio Guillermo de Jesús Aguirre Vera Maria P. Fernandez Gomez Jorge Zumaeta Andrés M. Alvarez-Pinzon |
| author_sort | José E. Valerio |
| collection | DOAJ |
| description | Parkinson’s disease (PD) is a progressive neurodegenerative disorder marked by motor and non-motor dysfunctions that severely compromise patients’ quality of life. While pharmacological treatments provide symptomatic relief in the early stages, advanced PD often requires neurosurgical interventions, such as deep brain stimulation (DBS) and focused ultrasound (FUS), for effective symptom management. A significant challenge in optimizing these therapeutic strategies is the early identification and recruitment of suitable candidates for clinical trials. This review explores the role of artificial intelligence (AI) in advancing neurosurgical and neuroscience interventions for PD, highlighting the ways in which AI-driven platforms are transforming clinical trial design and patient selection. Machine learning (ML) algorithms and big data analytics enable precise patient stratification, risk assessment, and outcome prediction, accelerating the development of novel therapeutic approaches. These innovations improve trial efficiency, broaden treatment options, and enhance patient outcomes. However, integrating AI into clinical trial frameworks presents challenges such as data standardization, regulatory hurdles, and the need for extensive validation. Addressing these obstacles will require collaboration among neurosurgeons, neuroscientists, AI specialists, and regulatory bodies to establish ethical and effective guidelines for AI-driven technologies in PD neurosurgical research. This paper emphasizes the transformative potential of AI and technological innovation in shaping the future of PD neurosurgery, ultimately enhancing therapeutic efficacy and patient care. |
| format | Article |
| id | doaj-art-1d19e67b72c5485fb3af2b49e7dba259 |
| institution | Kabale University |
| issn | 2076-3425 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj-art-1d19e67b72c5485fb3af2b49e7dba2592025-08-20T03:47:49ZengMDPI AGBrain Sciences2076-34252025-05-0115549410.3390/brainsci15050494AI-Driven Advances in Parkinson’s Disease Neurosurgery: Enhancing Patient Selection, Trial Efficiency, and Therapeutic OutcomesJosé E. Valerio0Guillermo de Jesús Aguirre Vera1Maria P. Fernandez Gomez2Jorge Zumaeta3Andrés M. Alvarez-Pinzon4Neurosurgery Innovation and Technology Division, Latinoamerica Valerio Foundation, Weston, FL 33331, USANeurosurgery Innovation and Technology Division, Latinoamerica Valerio Foundation, Weston, FL 33331, USANeurosurgery Innovation and Technology Division, Latinoamerica Valerio Foundation, Weston, FL 33331, USANeurosurgery Innovation and Technology Division, Latinoamerica Valerio Foundation, Weston, FL 33331, USANeurosurgery Innovation and Technology Division, Latinoamerica Valerio Foundation, Weston, FL 33331, USAParkinson’s disease (PD) is a progressive neurodegenerative disorder marked by motor and non-motor dysfunctions that severely compromise patients’ quality of life. While pharmacological treatments provide symptomatic relief in the early stages, advanced PD often requires neurosurgical interventions, such as deep brain stimulation (DBS) and focused ultrasound (FUS), for effective symptom management. A significant challenge in optimizing these therapeutic strategies is the early identification and recruitment of suitable candidates for clinical trials. This review explores the role of artificial intelligence (AI) in advancing neurosurgical and neuroscience interventions for PD, highlighting the ways in which AI-driven platforms are transforming clinical trial design and patient selection. Machine learning (ML) algorithms and big data analytics enable precise patient stratification, risk assessment, and outcome prediction, accelerating the development of novel therapeutic approaches. These innovations improve trial efficiency, broaden treatment options, and enhance patient outcomes. However, integrating AI into clinical trial frameworks presents challenges such as data standardization, regulatory hurdles, and the need for extensive validation. Addressing these obstacles will require collaboration among neurosurgeons, neuroscientists, AI specialists, and regulatory bodies to establish ethical and effective guidelines for AI-driven technologies in PD neurosurgical research. This paper emphasizes the transformative potential of AI and technological innovation in shaping the future of PD neurosurgery, ultimately enhancing therapeutic efficacy and patient care.https://www.mdpi.com/2076-3425/15/5/494Parkinson’s diseaseartificial intelligencemachine learningclinical trialsneurodegenerative disorders |
| spellingShingle | José E. Valerio Guillermo de Jesús Aguirre Vera Maria P. Fernandez Gomez Jorge Zumaeta Andrés M. Alvarez-Pinzon AI-Driven Advances in Parkinson’s Disease Neurosurgery: Enhancing Patient Selection, Trial Efficiency, and Therapeutic Outcomes Brain Sciences Parkinson’s disease artificial intelligence machine learning clinical trials neurodegenerative disorders |
| title | AI-Driven Advances in Parkinson’s Disease Neurosurgery: Enhancing Patient Selection, Trial Efficiency, and Therapeutic Outcomes |
| title_full | AI-Driven Advances in Parkinson’s Disease Neurosurgery: Enhancing Patient Selection, Trial Efficiency, and Therapeutic Outcomes |
| title_fullStr | AI-Driven Advances in Parkinson’s Disease Neurosurgery: Enhancing Patient Selection, Trial Efficiency, and Therapeutic Outcomes |
| title_full_unstemmed | AI-Driven Advances in Parkinson’s Disease Neurosurgery: Enhancing Patient Selection, Trial Efficiency, and Therapeutic Outcomes |
| title_short | AI-Driven Advances in Parkinson’s Disease Neurosurgery: Enhancing Patient Selection, Trial Efficiency, and Therapeutic Outcomes |
| title_sort | ai driven advances in parkinson s disease neurosurgery enhancing patient selection trial efficiency and therapeutic outcomes |
| topic | Parkinson’s disease artificial intelligence machine learning clinical trials neurodegenerative disorders |
| url | https://www.mdpi.com/2076-3425/15/5/494 |
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