Atmospheric pressure nonequilibrium plasma holds significant potential in biomedical applications due to its ability to generate reactive species at low temperatures. However, accurately quantifying and controlling plasma dosage remains challenging. Although equivalent total oxidation potential (ETOP) has been proposed for defining dosage, previous methods required measurement of various reactive oxygen and nitrogen species (RONS) densities, which are impractical in diverse plasma settings. Efficient ETOP prediction across variable conditions is thus essential. To address this, we propose a machine learning-based ETOP modeling method. This study collected RONS density data under various conditions using laser-induced fluorescence and trained an artificial neural network to predict ETOP values based on input parameters like voltage, gas flow rate, oxygen concentration, and humidity. This approach enables efficient ETOP prediction across variable conditions, supporting the standardization and clinical application of plasma medicine.

中文翻译：

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使用等效总氧化电位 （ETOP） 确定血浆剂量：通过机器学习从概念到实际应用


常压非平衡等离子体能够在低温下产生反应性物质，因此在生物医学应用中具有巨大潜力。然而，准确量化和控制血浆剂量仍然具有挑战性。尽管已经提出了等效总氧化电位 （ETOP） 来定义剂量，但以前的方法需要测量各种活性氧和氮物质 （RONS） 密度，这在不同的等离子体环境中是不切实际的。因此，在可变条件下进行有效的 ETOP 预测至关重要。为了解决这个问题，我们提出了一种基于机器学习的 ETOP 建模方法。本研究使用激光诱导荧光收集了各种条件下的 RONS 密度数据，并训练了一个人工神经网络，根据电压、气体流速、氧气浓度和湿度等输入参数预测 ETOP 值。这种方法能够在可变条件下进行高效的 ETOP 预测，支持血浆医学的标准化和临床应用。