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NeurostimML: A machine learning model for predicting neurostimulation-induced tissue damage

OBJECTIVE. The safe delivery of electrical current to neural tissue depends on many factors, yet previous methods for predicting tissue damage rely on only a few stimulation parameters. Here, we report the development of a machine learning approach that could lead to a more reliable method for predi...

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Autores principales: Li, Yi, Frederick, Rebecca A., George, Daniel, Cogan, Stuart F., Pancrazio, Joseph J., Bleris, Leonidas, Hernandez-Reynoso, Ana G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10614958/
https://www.ncbi.nlm.nih.gov/pubmed/37905012
http://dx.doi.org/10.1101/2023.10.18.562980
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author Li, Yi
Frederick, Rebecca A.
George, Daniel
Cogan, Stuart F.
Pancrazio, Joseph J.
Bleris, Leonidas
Hernandez-Reynoso, Ana G.
author_facet Li, Yi
Frederick, Rebecca A.
George, Daniel
Cogan, Stuart F.
Pancrazio, Joseph J.
Bleris, Leonidas
Hernandez-Reynoso, Ana G.
author_sort Li, Yi
collection PubMed
description OBJECTIVE. The safe delivery of electrical current to neural tissue depends on many factors, yet previous methods for predicting tissue damage rely on only a few stimulation parameters. Here, we report the development of a machine learning approach that could lead to a more reliable method for predicting electrical stimulation-induced tissue damage by incorporating additional stimulation parameters. APPROACH. A literature search was conducted to build an initial database of tissue response information after electrical stimulation, categorized as either damaging or non-damaging. Subsequently, we used ordinal encoding and random forest for feature selection, and investigated four machine learning models for classification: Logistic Regression, K-nearest Neighbor, Random Forest, and Multilayer Perceptron. Finally, we compared the results of these models against the accuracy of the Shannon equation. MAIN RESULTS. We compiled a database with 387 unique stimulation parameter combinations collected from 58 independent studies conducted over a period of 47 years, with 195 (51%) categorized as non-damaging and 190 (49%) categorized as damaging. The features selected for building our model with a Random Forest algorithm were: waveform shape, geometric surface area, pulse width, frequency, pulse amplitude, charge per phase, charge density, current density, duty cycle, daily stimulation duration, daily number of pulses delivered, and daily accumulated charge. The Shannon equation yielded an accuracy of 63.9% using a k value of 1.79. In contrast, the Random Forest algorithm was able to robustly predict whether a set of stimulation parameters was classified as damaging or non-damaging with an accuracy of 88.3%. SIGNIFICANCE. This novel Random Forest model can facilitate more informed decision making in the selection of neuromodulation parameters for both research studies and clinical practice. This study represents the first approach to use machine learning in the prediction of stimulation-induced neural tissue damage, and lays the groundwork for neurostimulation driven by machine learning models.
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spelling pubmed-106149582023-10-31 NeurostimML: A machine learning model for predicting neurostimulation-induced tissue damage Li, Yi Frederick, Rebecca A. George, Daniel Cogan, Stuart F. Pancrazio, Joseph J. Bleris, Leonidas Hernandez-Reynoso, Ana G. bioRxiv Article OBJECTIVE. The safe delivery of electrical current to neural tissue depends on many factors, yet previous methods for predicting tissue damage rely on only a few stimulation parameters. Here, we report the development of a machine learning approach that could lead to a more reliable method for predicting electrical stimulation-induced tissue damage by incorporating additional stimulation parameters. APPROACH. A literature search was conducted to build an initial database of tissue response information after electrical stimulation, categorized as either damaging or non-damaging. Subsequently, we used ordinal encoding and random forest for feature selection, and investigated four machine learning models for classification: Logistic Regression, K-nearest Neighbor, Random Forest, and Multilayer Perceptron. Finally, we compared the results of these models against the accuracy of the Shannon equation. MAIN RESULTS. We compiled a database with 387 unique stimulation parameter combinations collected from 58 independent studies conducted over a period of 47 years, with 195 (51%) categorized as non-damaging and 190 (49%) categorized as damaging. The features selected for building our model with a Random Forest algorithm were: waveform shape, geometric surface area, pulse width, frequency, pulse amplitude, charge per phase, charge density, current density, duty cycle, daily stimulation duration, daily number of pulses delivered, and daily accumulated charge. The Shannon equation yielded an accuracy of 63.9% using a k value of 1.79. In contrast, the Random Forest algorithm was able to robustly predict whether a set of stimulation parameters was classified as damaging or non-damaging with an accuracy of 88.3%. SIGNIFICANCE. This novel Random Forest model can facilitate more informed decision making in the selection of neuromodulation parameters for both research studies and clinical practice. This study represents the first approach to use machine learning in the prediction of stimulation-induced neural tissue damage, and lays the groundwork for neurostimulation driven by machine learning models. Cold Spring Harbor Laboratory 2023-10-21 /pmc/articles/PMC10614958/ /pubmed/37905012 http://dx.doi.org/10.1101/2023.10.18.562980 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
spellingShingle Article
Li, Yi
Frederick, Rebecca A.
George, Daniel
Cogan, Stuart F.
Pancrazio, Joseph J.
Bleris, Leonidas
Hernandez-Reynoso, Ana G.
NeurostimML: A machine learning model for predicting neurostimulation-induced tissue damage
title NeurostimML: A machine learning model for predicting neurostimulation-induced tissue damage
title_full NeurostimML: A machine learning model for predicting neurostimulation-induced tissue damage
title_fullStr NeurostimML: A machine learning model for predicting neurostimulation-induced tissue damage
title_full_unstemmed NeurostimML: A machine learning model for predicting neurostimulation-induced tissue damage
title_short NeurostimML: A machine learning model for predicting neurostimulation-induced tissue damage
title_sort neurostimml: a machine learning model for predicting neurostimulation-induced tissue damage
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10614958/
https://www.ncbi.nlm.nih.gov/pubmed/37905012
http://dx.doi.org/10.1101/2023.10.18.562980
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