Machine learning for the localization of Subthalamic Nucleus during deep brain stimulation surgery: a systematic review and Meta-analysis.

Autor: Inggas MAM; Department of Neurosurgery, Universitas Pelita Harapan, Tangerang, Banten, Indonesia. made.inggas@lecturer.uph.edu., Coyne T; Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia., Taira T; Department of FUS Center, Moriyama Neurosurgical Center Hospital, Tokyo, Japan., Karsten JA; Department of Neurosurgery, Universitas Pelita Harapan, Tangerang, Banten, Indonesia., Patel U; Mayo Clinic, Jacksonville, FL, USA., Kataria S; Department of Neurology, Louisiana State University Health Science Center at Shreveport, Louisiana, CA, USA., Putra AW; Department of Medicine, Universitas Trisakti, Jakarta, Indonesia., Setiawan J; Department of Neurosurgery, Universitas Pelita Harapan, Tangerang, Banten, Indonesia., Tanuwijaya AW; Department of Medicine, Faculty of Medicine, Universitas Katolik Indonesia Atma Jaya, Jakarta, Indonesia., Wong E; Department of Neurosurgery, Universitas Pelita Harapan, Tangerang, Banten, Indonesia., Pitliya A; Department of Medicine, Pamnani Hospital and Research Center, Mandsaur, MP, India., Tjahyanto T; Department of Medicine, Universitas Tarumanagara, Jakarta, Indonesia., Wijaya JH; Department of Neurosurgery, Universitas Pelita Harapan, Tangerang, Banten, Indonesia. Jeremiah.hansum6@gmail.com.
Jazyk: angličtina
Zdroj: Neurosurgical review [Neurosurg Rev] 2024 Oct 10; Vol. 47 (1), pp. 774. Date of Electronic Publication: 2024 Oct 10.
DOI: 10.1007/s10143-024-03010-x
Abstrakt: Introduction: Delineating subthalamic nucleus (STN) boundaries using microelectrode recordings (MER) and trajectory history is a valuable resource for neurosurgeons, aiding in the accurate and efficient positioning of deep brain stimulation (DBS) electrodes within the STN. Here, we aimed to assess the application of artificial intelligence, specifically Hidden Markov Models (HMM), in the context of STN localization.
Methods: A comprehensive search strategy was employed, encompassing electronic databases, including PubMed, EuroPMC, and MEDLINE. This search strategy entailed a combination of controlled vocabulary (e.g., MeSH terms) and free-text keywords pertaining to "artificial intelligence," "machine learning," "deep learning," and "deep brain stimulation." Inclusion criteria were applied to studies reporting the utilization of HMM for predicting outcomes in DBS, based on structured patient-level health data, and published in the English language.
Results: This systematic review incorporated a total of 14 studies. Various machine learning compared wavelet feature to proposed features in diagnosing the STN, with the HMM yielding a diagnostic odds ratio (DOR) of 838.677 (95% CI: 203.309-3459.645). Similarly, the K-Nearest Neighbors (KNN) model produced parameter estimates, including a diagnostic odds ratio of 25.151 (95% CI: 12.270-51.555). Meanwhile, the support vector machine (SVM) model exhibited parameter estimates, with a DOR of 13.959 (95% CI: 10.436-18.671).
Conclusions: MER data demonstrates significant variability in neural activity, with studies employing a wide range of methodologies. Machine learning plays a crucial role in aiding STN diagnosis, though its accuracy varies across different approaches.
(© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
Databáze: MEDLINE
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