Despite the huge complexity of the foreign body reaction, a quantitative assessment over time of the scar tissue thickness around implanted materials is needed to figure out the evolution of neural implants for long times. A data-driven approach, based on phenomenological polynomial functions, is able to reproduce experimental data. Nevertheless, a misuse of this strategy may lead to unsatisfactory results, even if standard indexes are optimized. In this work, an effective in silico procedure was presented to reproduce the scar tissue dynamics around implanted synthetic devices and to predict the capsule thickness for times before and after experimental detections.

中文翻译：

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一种数据驱动的多项式方法，可重现神经植入物周围的疤痕组织生长。

尽管异物反应的复杂性很高，但仍需要对植入材料周围疤痕组织厚度进行长期定量评估，以弄清神经植入物的长期演变。基于现象学多项式函数的数据驱动方法能够重现实验数据。但是，即使对标准指标进行了优化，滥用此策略也可能导致结果不理想。在这项工作中，提出了一种有效的计算机程序，可重现植入的合成装置周围的疤痕组织动态，并预测实验检测前后囊的厚度。