Tissue engineering (TE) has emerged as a promising field for regenerating damaged tissues and organs, but traditional design approaches often rely on trial-and-error methods, leading to inefficiencies in scaffold development and biomaterial optimization. This paper explores the integration of interpretable artificial intelligence (AI) and machine learning (ML) models, enhanced by feature engineering techniques, to improve TE design. We review related work and existing systems, propose a novel framework utilizing XGBoost with SHapley Additive exPlanations (SHAP) for predicting scaffold biocompatibility, and present simulated results demonstrating improved accuracy and interpretability. The proposed system addresses key challenges such as data complexity and model transparency, paving the way for more efficient TE advancements. Our findings suggest that feature-engineered interpretable models can reduce development time by up to 30% while providing actionable insights into biomaterial properties.
Shaik et al. (Sat,) studied this question.