Digital Twin Dentures Adapting Occlusion Based on Chewing Behavior Collected Through Embedded Sensors

Abstract

Digital twin dentures represent an emerging advancement in prosthodontics by combining digital modeling, embedded sensor technology, and artificial intelligence to create adaptive removable prostheses capable of responding to an individual’s chewing behavior. Unlike conventional dentures, these smart prostheses continuously collect functional data such as bite force, chewing frequency, occlusal contacts, and mandibular movement through integrated microsensors. The acquired information is transmitted to a virtual digital twin that replicates the clinical performance of the denture, enabling continuous assessment of occlusal function and personalized treatment planning. Advanced data analytics facilitate the identification of functional imbalances, excessive loading, and parafunctional habits, allowing clinicians to optimize occlusion and improve prosthesis performance over time. This adaptive approach has the potential to enhance masticatory efficiency, patient comfort, prosthesis stability, and long-term durability while supporting preventive maintenance and reducing the frequency of manual adjustments. Furthermore, integration with cloud-based monitoring systems and digital dentistry workflows may enable remote follow-up and evidence-based clinical decision-making. Although challenges remain regarding sensor reliability, data security, manufacturing complexity, and clinical validation, digital twin dentures offer a promising direction for personalized prosthodontic care by transforming static prostheses into intelligent systems capable of continuous functional adaptation and improved patient-centered outcomes.