From 3D reconstruction to simulation
In dentistry, a digital twin is a computational model that replicates the anatomical structures of an individual patient and updates as new data becomes available. According to Agenda Digitale, the raw material comes from intraoral scanners, panoramic radiographs, and cone-beam computed tomography, from which teeth, alveolar bone, periodontal ligament, and the temporomandibular joint are reconstructed. Three-dimensional reconstruction, the source notes, is not enough: the term digital twin applies when the model is used to simulate the behavior of structures under mechanical stresses, such as masticatory load or the force of an aligner.
The tool that enables this transition is finite element analysis, an engineering method that breaks a complex structure down into smaller elements and calculates each one's reaction to an applied force. In dentistry, it has been used for decades, but almost always on generic or semi-generic models; the ongoing innovation is to build models based on the real data of an individual patient, with a growing role for artificial intelligence. The source places dentistry at an early stage, consisting more of prototypes and conceptual studies than of tools ready for daily clinical use, unlike cardiology and orthopedics, which have used patient-specific simulations for several years.
Aligners and implants: what the studies say
The most studied case is therapy with clear aligners. The clinical problem is that the tooth does not move exactly as planned by the software: the expression rate—that is, the ratio of actual to planned movement—ranges between 55% and 72% depending on the type of displacement. Some movements are relatively predictable, such as distalization or rotation around their own axis; others are much less so, such as intrusion, torque, or transverse expansion. It is this gap between plan and reality that the digital twin aims to address, compensating in advance with over-treatment calibrated to the expected loss.
The source cites several studies. Zhang and colleagues used a recursive finite element method to simulate incisor retraction after extraction, addressing the so-called roller-coaster effect—the nonlinear pattern in which teeth tip and then upright during space closure; in tests, the adaptive approach reduced unwanted tipping from more than 6° to less than 1°. Li and colleagues, by contrast, studied aligner thickness: increasing from 0.5 to 0.75 millimeters improves the efficiency of maxillary arch expansion but increases stress on periodontal tissues, and a sequential strategy on posterior teeth proves better than a simultaneous one. At the University of Copenhagen, Kenny Erleben's group built a database of digital dental patients from high-resolution CT scans, also useful as a training environment for artificial intelligence algorithms.
Other contributions concern orthodontic attachments, the composite protrusions that give aligners grip: simulations show that their wear over time reduces movement efficiency, especially when they play a primary control role, suggesting more frequent monitoring and possible repositioning. In oral surgery, Xing and colleagues modeled the tooth–periodontal ligament complex to quantify the torsion angle beyond which the root risks fracturing, considering curvature, width, length, and bone type; the model was also clinically validated.
The stated limitations
The source identifies validation, costs, interoperability, data protection, and continuous updating of models as decisive obstacles. These are conditions listed in the original article, not quantitative assessments: the available material does not specify adoption timelines, investments, or regulatory references. It therefore remains to be seen how these systems will be validated on a large scale and how patient data will be handled and made compatible across different platforms.
