Machine Learning Approach to Investigating Macrophage Polarization on Various Titanium Surface Characteristics.

Objective: Current laboratory studies on the effect of biomaterial properties on immune reactions are incomplete and based on a single or a few combination features of the biomaterial design. This study utilizes intelligent prediction models to explore the key features of titanium implant materials in macrophage polarization. Impact Statement: This pilot study provided some insights into the great potential of machine learning in exploring bone immunomodulatory biomaterials. Introduction: Titanium materials are commonly utilized as bone replacement materials to treat missing teeth and bone defects. The immune response caused by implant materials after implantation in the body has a double-edged sword effect on osseointegration. Macrophage polarization has been extensively explored to understand early material-mediated immunomodulation. However, understanding of implant material surface properties and immunoregulations remains limited due to current experimental settings, which are based on trial-by-trial approaches. Artificial intelligence, with its capacity to analyze large datasets, can help explore complex material-cell interactions. Methods: In this study, the effect of titanium surface properties on macrophage polarization was analyzed using intelligent prediction models, including random forest, extreme gradient boosting, and multilayer perceptron. Additionally, data extracted from the newly published literature were further input into the trained models to validate their performance. Results: The analysis identified "cell seeding density", "contact angle", and "roughness" as the most important features regulating interleukin 10 and tumor necrosis factor α secretion. Additionally, the predicted interleukin 10 levels closely matched the experimental results from newly published literature, while the tumor necrosis factor α predictions exhibited consistent trends. Conclusion: The polarization response of macrophages seeded on titanium materials is influenced by multiple factors, and artificial intelligence can assist in extracting the key features of implant materials for immunoregulation.