A new approach to bioceramics based on tissue reaction of tricalcium phosphate for biomedical and sport applications using machine learning modeling

Tricalcium phosphate (TCP) bioceramics have emerged as a promising option to meet the specific requirements associated with athlete bone fractures. Advanced computational modeling techniques, such as artificial neural networks (ANNs), have been leveraged to optimize the formulation, structural properties, and implantation strategies of TCP-based biomaterials. This personalized approach enables the tailoring of TCP implants and scaffolds to match the specific biomechanical and biological requirements of individual athletes, maximizing the potential for successful bone regeneration and a timely return to athletic competition. The strategic application of TCP-based biomaterials, combined with personalized computational modeling, holds great promise in revolutionizing the management of athlete bone fractures. This article investigates the use of an ANN to understand the complex relationships between various parameters, such as porosity and bone growth, and their effects on the biodegradation rate, compressive strength, and hardness of TCP-based bioceramics. The accuracy of the neural network's predictions was validated using linear regression analysis, confirming its applicability in guiding the design and optimization of these biomaterials for sports-related bone injury applications. The study developed an artificial neural network (ANN) model to accurately predict the biodegradation rate, compressive strength, and hardness of tricalcium phosphate (TCP) bioceramics as a function of porosity and bone growth. The ANN demonstrated high accuracy in forecasting these key bioceramic properties.