A data-driven polynomial approach to reproduce the scar tissue outgrowth around neural implants

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.