Digital twin simulation to predict orthodontic relapse before treatment completion

Abstract

Orthodontic relapse remains one of the greatest challenges in achieving long-term treatment stability, often resulting from complex interactions among biological remodeling, biomechanical forces, patient compliance, and retention strategies. Recent advances in digital dentistry have introduced digital twin technology as a transformative approach for creating dynamic, patient-specific virtual models that continuously integrate clinical, imaging, and treatment data throughout orthodontic care. By combining three-dimensional imaging, intraoral scanning, artificial intelligence, biomechanical simulation, and predictive analytics, digital twins enable clinicians to monitor treatment progression in real time and forecast the likelihood of relapse before active treatment is completed. This proactive approach supports individualized treatment planning, optimization of force application, timely modification of therapeutic strategies, and personalized retention protocols to improve long-term outcomes. Digital twin simulation also enhances clinical decision-making by providing visual representations of anticipated tooth movement and treatment responses, thereby facilitating better communication between clinicians and patients. Although challenges related to data integration, computational demands, model validation, interoperability, and ethical management of patient information remain, ongoing developments in artificial intelligence and digital technologies continue to strengthen the feasibility of clinical implementation. Digital twin simulation has the potential to redefine predictive orthodontics by enabling precision-based, adaptive, and preventive treatment strategies that minimize relapse risk while improving treatment efficiency, stability, and patient-centered care.