Current practice and application of finite element analysis in clear aligners: A scoping review
Bibliographic record
Abstract
Clear aligner therapy (CAT) has become increasingly popular in orthodontics; however, concerns persist about its predictability and effectiveness. The finite element method (FEM) has become an important tool for studying aligner biomechanics and force delivery, offering insights that are often hard to obtain clinically. This scoping review aimed to map FEM applications in CAT research and critically assess their methodological approaches, highlighting strengths, limitations, and areas for improvement. Six electronic databases were searched up to June 2, 2025. A total of 448 records were screened, resulting in 149 full-text articles, of which 50 met the inclusion criteria. Studies were examined concerning model development, representation of the periodontal ligament (PDL) and bone, material property assignment, meshing strategies, boundary conditions, contact interactions, and post-processing analyses. Significant methodological variation was observed. About half of the studies modeled bone as a single structure, whereas others distinguished cortical from cancellous bone. Most assumed a linear, isotropic PDL, although some employed bilinear or nonlinear viscoelastic models. Boundary conditions usually fixed the maxilla and mandible, while contact constraints connected biologically linked tissues. Meshing strategies differed between native and external software, with tetrahedral elements being most common. Displacement was assessed using either global or local coordinate systems, influencing clinical interpretation. Stress analysis was primarily limited to von Mises stress, although a few reports also included principal stresses. Validation was infrequent, with only isolated attempts using radiological or in vitro data. FEM offers useful insights into CAT biomechanics, but its conclusions are constrained by methodological differences and a lack of validation. Developing standardized protocols is crucial for enhancing reproducibility and clinical applicability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".