Machine Learning's Application in Deep Brain Stimulation for Parkinson's Disease: A Review

Overview

Journal Brain Sci

Publisher MDPI

Date 2020 Nov 3

PMID 33139614

Citations 13

Authors

Jeremy Watts

Anahita Khojandi

Oleg Shylo

Ritesh A Ramdhani

Affiliations

Soon will be listed here.

Abstract

Deep brain stimulation (DBS) is a surgical treatment for advanced Parkinson's disease (PD) that has undergone technological evolution that parallels an expansion in clinical phenotyping, neurophysiology, and neuroimaging of the disease state. Machine learning (ML) has been successfully used in a wide range of healthcare problems, including DBS. As computational power increases and more data become available, the application of ML in DBS is expected to grow. We review the literature of ML in DBS and discuss future opportunities for such applications. Specifically, we perform a comprehensive review of the literature from PubMed, the Institute for Scientific Information's Web of Science, Cochrane Database of Systematic Reviews, and Institute of Electrical and Electronics Engineers' (IEEE) Xplore Digital Library for ML applications in DBS. These studies are broadly placed in the following categories: (1) DBS candidate selection; (2) programming optimization; (3) surgical targeting; and (4) insights into DBS mechanisms. For each category, we provide and contextualize the current body of research and discuss potential future directions for the application of ML in DBS.

Citing Articles

Model-based closed-loop control of thalamic deep brain stimulation.

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A feasibility study on AI-controlled closed-loop electrical stimulation implants.

Eickhoff S, Garcia-Agundez A, Haidar D, Zaidat B, Adjei-Mosi M, Li P Sci Rep. 2023; 13(1):10163.

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Artificial Intelligence Applications for Assessment, Monitoring, and Management of Parkinson Disease Symptoms: Protocol for a Systematic Review.

Bounsall K, Milne-Ives M, Hall A, Carroll C, Meinert E JMIR Res Protoc. 2023; 12:e46581.

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