Development of a Machine-Learning-Driven Microneedle Design Methodology for Biological Tissue Grippers

Abstract

Microneedles offer promising capabilities not only for minimally invasive drug delivery but also as effective bio-tissue grippers. However, achieving strong tissue fixation while minimizing tissue damage during insertion remains a significant challenge. In this study, we propose a novel microneedle geometry optimized for 3D printing, designed to maximize the Pull-Out-to-Penetration Ratio through a machine-learning-based optimization framework combined with finite element analysis. Experimental results show that the optimized geometry achieves a six-fold improvement in the objective metric relative to conventional conical designs, demonstrating enhanced tissue fixation while simultaneously reducing insertion-induced damage. This approach highlights the potential for customizable, low-pain microneedle designs across a broad range of biomedical applications.