Evidence map›Paper›PMID 40125695›Full record

ReviewOrthodontics & craniofacial research2025

How AI-Driven Root and Bone Predictions Can Assist Clear Aligner Treatment Planning.

Eser Tüfekçi, Caroline K Carrico, Christina B Gordon, Steven J Lindauer

Abstract readReview
In one paragraph

Review in Orthodontics & craniofacial research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Eser TüfekçiDepartment of Orthodontics, Virginia Commonwealth University, School of Dentistry, Richmond, Virginia, USA.ORCID https://orcid.org/0000-0002-9379-7316
Caroline K CarricoDepartment of Dental Public Health and Policy, Virginia Commonwealth University, School of Dentistry, Richmond, Virginia, USA.
Christina B GordonDepartment of Orthodontics, Virginia Commonwealth University, School of Dentistry, Richmond, Virginia, USA.
Steven J LindauerDepartment of Orthodontics, Virginia Commonwealth University, School of Dentistry, Richmond, Virginia, USA.

Funding

Virginia Commonwealth University, AD Williams Research Fund
6 · The paper itself

Abstract

Integrating artificial intelligence (AI) and advanced three-dimensional (3D) imaging has revolutionised dentistry by enhancing diagnostics and treatment planning. Advanced algorithms and machine-learning techniques may enable orthodontists to analyse complex cases and predict treatment outcomes accurately. This technology facilitates the creation of customised treatment plans that consider individual tooth morphology and periodontal health, optimising force application and minimising treatment time. Since their introduction, clear aligners have gained popularity, with over 17 million people treated by 2023. Compared with fixed appliances, clear aligners offer advantages, such as better aesthetics, comfort and oral hygiene. Treating patients with a compromised periodontium requires accurate diagnosis and treatment planning. This paper reviews how AI-driven treatment planning software predicting root movement and visualising bone structures may impact treatment decisions and, ultimately, treatment outcomes. The technology behind machine learning and AI in designing clear aligners is discussed. Research shows that when viewing the cases in 3D, clinicians are more comfortable when treating crowding cases with a non-extraction approach using interproximal reduction (IPR) only. However, it was interesting to note that clinicians with extensive experience treating clear aligner patients were more comfortable using IPR to address severe crowding cases when viewed in 2D, compared with those less experienced with clear aligners. However, when the cases were visualised in 3D, both groups showed equal comfort in using IPR, as the roots were within the bone. AI-driven treatment planning software, utilising machine learning in conjunction with 3D modelling, may enhance the predictability of orthodontic movements while reducing treatment time and increasing patient satisfaction.

Indexed as

Artificial IntelligenceOrthodontic Appliance DesignPatient Care PlanningTooth Movement TechniquesTooth RootHumansImaging, Three-DimensionalMachine LearningMalocclusion

Identifiers

PMID40125695
PMCPMC12927130

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.