Machine learning-driven inverse design of puncture needles with tailored mechanics

In minimally invasive surgery, designing puncture needles with customizable structures to achieve personalized puncture performance is a significant challenge. Existing reverse design methods struggle to capture the complex nonlinear behavior of needle-tissue interactions.

This study proposes a machine-learning-based reverse design method aimed at achieving precise customization of needle mechanical behavior. We developed a rapid reverse design framework integrating machine learning and finite element analysis, capable of directly generating optimal structural parameters from target puncture force-penetration depth curves. Through training on large-scale finite element simulation data, deep learning neural network models captured the complex mapping relationship between needle structure and mechanical response.

In rigorous cross-validation, the prediction results showed normalized root mean square errors (NRMSE) of 0.06381 and 0.06234 compared to the target curves and finite element analysis, respectively. The model achieved 98.2% classification accuracy for curve types, with loss functions converging to optimal values after sufficient training epochs.

This approach demonstrates high accuracy and robustness in needle-design customization. It not only opens new avenues for rapid, customized design of puncture needles but also provides an innovative paradigm for intelligent design of complex medical devices, potentially advancing precision medicine technologies and shortening design cycles.