Predictive Neural Network Modeling of Nanoporous Anodic Alumina for Controlled Drug Release Implants: An Integrated Machine Learning Approach

Background: Nanoporous anodic alumina (NAA) has emerged as a promising platform for localized drug delivery in biomedical implants owing to its tunable nanoscale pore structure and biocompatibility. However, achieving the desired pore characteristics currently relies on time-consuming trial-and-error adjustments of anodization parameters. Methods: We developed a comprehensive data-driven machine learning framework using a feed-forward artificial neural network (ANN) with three hidden layers (64-32-16 neurons) trained on 77 samples from a compiled dataset of 99 anodization experiments spanning 1995–2025. The model predicts the NAA pore diameter based on anodization conditions (electrolyte type, concentration, voltage, temperature, and time). Results: The ANN achieved R2 = 0.803, root mean square error (RMSE) = 25.83 nm, and mean absolute error (MAE) = 17.05 nm on training data; however, 5-fold cross-validation revealed moderate generalization (CV R2 = 0.471 ± 0.078). Multiple linear regression showed comparable training performance (R2 = 0.804) but superior cross-validation (CV R2 = 0.729 ± 0.083). Feature importance analysis identified anodization voltage (29.15% ANN importance) and electrolyte type (30.23%) as the most influential factors. Coupling ANN-predicted pore dimensions with Higuchi diffusion modeling demonstrated that the pore diameter increased from 50 to 100 nm, nearly doubling the initial release rates (8 to 11 h−1) and reducing the time to 50% release from 39.1 to 20.7 h. Conclusions: This data-driven approach offers a powerful tool to reduce experimental iteration and accelerate the development of advanced drug-delivery implants by enabling the rational design of NAA pore structures for optimized drug loading and release kinetics.