NeurostimML: A machine learning model for predicting neurostimulation-induced tissue damage.
NeurostimML: A machine learning model for predicting neurostimulation-induced tissue damage.
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NeurostimML：一种用于预测神经刺激引起的组织损伤的机器学习模型。
DOI：
10.1101/2023.10.18.562980
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Hernandez-Reynoso,AnaG
中科院分区：
文献类型：
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作者：
Li,Yi;Frederick,RebeccaA;George,Daniel;Cogan,StuartF;Pancrazio,JosephJ;Bleris,Leonidas;Hernandez-Reynoso,AnaG
目的电流向神经组织的安全传输依赖于许多因素，而以往预测组织损伤的方法仅依赖于少数刺激参数。在这里，我们报告了一种机器学习方法的发展，通过结合额外的刺激参数，可以导致一种更可靠的方法来预测电刺激引起的组织损伤。方法通过文献搜索来建立电刺激后组织反应信息的初始数据库，将其归类为损伤或非损伤。随后，我们使用有序编码和随机森林进行特征选择，并研究了四种机器学习模型用于分类：Logistic回归、K近邻、随机森林和多层感知器。最后，我们将这些模型的结果与香农方程的准确性进行了比较。主要结果我们编制了一个数据库，其中387个独特的刺激参数组合来自47年来进行的58个独立研究，其中195个(51%)被归类为非破坏性，190个(49%)被归类为破坏性。使用随机森林算法建立模型所选择的特征是：波形形状、几何表面积、脉冲宽度、频率、脉冲幅度、每相电荷、电荷密度、电流密度、占空比、每日刺激持续时间、每日递送脉冲数量和每日累积电荷。当k值为1.79时，香农方程的准确率为63.9%。相比之下，随机森林算法能够稳健地预测一组刺激参数是损伤还是非损伤，准确率为88.3%。意义这种新的随机森林模型可以为研究和临床实践中选择神经调节参数提供更明智的决策。这项研究首次将机器学习用于预测刺激诱导的神经组织损伤，并为机器学习模型驱动的神经刺激奠定了基础。
ObjectiveThe 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.ApproachA 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 ResultsWe 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%.SignificanceThis 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.