Macrophage Phenotype Detection Methodology on Textured Surfaces via Nuclear Morphology Using Machine Learning

Macrophages play a crucial role in innate immunity, and their response to biomaterials significantly influences the clinical outcomes of implantable medical devices. Modulating macrophage phenotype is essential; however, traditional classification methods rely heavily on surface markers and cytokine profiling, which are labor‐intensive, highly variable, and difficult to standardize. Machine learning offers a powerful alternative by enabling phenotype classification based on intrinsic cellular morphology, including nuclear shape, cytoskeletal organization, and autofluorescence patterns. In this study, we develop a generalizable machine learning‐based approach to classify macrophage phenotypes using only nuclear deformation parameters, able to classify macrophages with 98% accuracy, eliminating the need for additional cellular markers while maintaining high accuracy and robustness even on patterned surfaces.