Digital twin technology for personalized endodontic treatment planning using CBCT, intraoral scans, and patient-specific biomechanical simulations

Abstract

Digital twin technology is emerging as a transformative approach in personalized healthcare by creating
a dynamic virtual representation of an individual patient that continuously integrates clinical, anatomical,
and biological data. In endodontics, the combination of cone-beam computed tomography (CBCT), intraoral
scanning, and patient-specific biomechanical simulations enables clinicians to construct highly accurate
digital replicas of teeth and surrounding structures for treatment planning. These virtual models facilitate
detailed visualization of root canal anatomy, simulation of instrumentation and obturation procedures,
assessment of stress distribution, and prediction of potential treatment outcomes before clinical
intervention. Artificial intelligence further enhances the digital twin by automating image segmentation,
integrating electronic dental records, and continuously updating patient-specific models throughout
treatment. This technology has the potential to improve diagnostic precision, optimize treatment strategies,
minimize procedural complications, and support personalized clinical decision-making. Additionally, digital
twins may facilitate long-term monitoring of healing and restoration performance through continuous data
integration. Despite promising developments, widespread implementation remains limited by challenges
related to data standardization, computational complexity, interoperability, regulatory considerations, and
validation in large-scale clinical studies. Continued advances in artificial intelligence, imaging technologies,
and computational modeling are expected to accelerate the clinical adoption of digital twin systems,
ultimately supporting predictive, precision, and patient-centered endodontic care.