Development of an ML-based design tool for predicting optimal lattice configurations for patient-specific endoprostheses

Lattice structures have gained significant attention in orthopaedic endoprosthesis design due to their ability to reduce stiffness mismatch, enhance osseointegration and improve longevity. However, optimising lattice structures for mechanical performance, manufacturability, and patient-specific requirements remains computationally intensive and time-consuming using traditional topology optimisation or generative design approaches. This study presents an ML-based design tool that predicts the optimal unit cell type and cell orientation of open-pore Ti-6Al-4V lattice structures based on predefined inputs, including the patient’s weight, bone porosity and mechanical constraints. A dataset of 40,960 finite element (FE) simulation results was used to train an Extra-Trees machine learning (ML) model, which achieved a classification accuracy of 100% and an F1-score of 0.996 in selecting the optimal lattice configuration. The model was integrated into a low-code design environment (Synera), enabling rapid generation of lattice structures without manual iteration. Compared to conventional methods, the proposed Artificial Intelligence (AI) approach could significantly reduce design time while maintaining high fidelity in achieving target performance. This tool enhances scalability and supports future clinical customisation by integrating patient-specific parameters. The study demonstrates the potential of ML to transform the lattice design process and lays the groundwork for faster, more adaptive, and more individualised orthopaedic endoprosthesis solutions.