In this study, a machine learning approach was applied to evaluate the impact of operating and design variables on dry powder inhalation efficiency. Emitted dose and fine particle fraction data were extracted from the literature for a variety of drug and carrier combinations. Carrier surface properties are obtained by image analysis of SEM images reported. Models combining artificial neural network and genetic algorithm were developed to determine the emitted dose and fine particle fraction. Design strategies for the carrier surface were also proposed to enhance the fine particle fractions.

中文翻译：

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基于载体的干粉吸入中载体表面设计的机器学习方法

在这项研究中，采用了机器学习方法来评估操作和设计变量对干粉吸入效率的影响。从文献中提取了各种药物和载体组合的发射剂量和细颗粒分数数据。载体表面性质是通过对报道的SEM图像进行图像分析而获得的。建立了将人工神经网络和遗传算法相结合的模型，以确定发射剂量和细颗粒分数。还提出了载体表面的设计策略，以提高细颗粒的比例。