Machine Learning-Assisted Evaluation of Topography-Dependent Cellular Responses in Meniscus-Mimetic Piezoelectric Nanofibrous Scaffolds

Electrospun nanofibrous scaffolds provide topographical cues that regulate cellular behavior; however, relationships between scaffold design and multidimensional biological responses remain difficult to define because structural and material parameters act simultaneously. Here, we fabricated meniscus-mimetic piezoelectric nanofibrous scaffolds with different fiber architectures and compositions and systematically evaluated their cellular responses using machine learning (ML). Poly(vinylidene fluoride) (PVDF)-based scaffolds with random, aligned, and radial fiber architectures were prepared, including composite scaffolds containing barium titanate (BaTiO3) and multi-walled carbon nanotubes (MWCNTs). Scaffold morphology and fiber organization were quantitatively characterized, and mesenchymal stem cells (MSCs) cultured on the scaffolds were analyzed by DAPI, F-actin, and SRY-box transcription factor 9 (SOX9) fluorescence imaging. Quantitative image analysis revealed scaffold-dependent differences in cellularity, nuclear morphology and orientation, cytoskeletal organization, and SOX9-associated phenotype, demonstrating distinct cellular responses to fiber topography and material composition. Scaffold and cellular parameters were subsequently integrated into a quantitative dataset to develop and compare multiple ML models for modeling scaffold-dependent cellular responses. The ML framework captured relationships between scaffold characteristics and multidimensional biological outcomes. These findings establish an experimental–computational approach for linking electrospun scaffold design with cellular behavior and provide a data-driven framework for the rational evaluation and optimization of meniscus-mimetic piezoelectric scaffolds.