AI based Prediction Model and Influential Factors for Hypersensitivity in Dental Implants made of Ti alloys

Abstract

This paper analysed influential factors governing the occurrence of hypersensitivity issues in patients with dental implants made of titanium (Ti) alloys. We presented tribological mechanisms affecting wear and fatigue in Ti alloys, outlined a method for identifying initial fatigue damage, and analysed AI-supported computational model to predict wear using experimentally obtainable input parameters. Wear debris from Ti alloy implant can trigger immune response. Fatigue cracks, driven by the material's behavior under long-term cyclic loading and harsh dental conditions, are identified using Small Crack Theory, an essential procedure for critical small-scale structures like Ti alloy implants. Various factors influence the formation of nano/micro-cracks and wear debris in implants, while computational models and emerging IoT-based in vivo sensing offer valuable tools for predicting loading regimes and enabling real-time, customised implant design.