Evidence map›Paper›PMID 41222734›Full record

ArticleClinical oral investigations2025

AI-Assisted 3D diagnosis of impacted maxillary canines: A validation study.

Sara Tinawi, Rodrigo Teixeira, Aron Aliaga-Del Castillo, Erika Benavides, Fabiana Soki, Antonio Carlos De Oliveira Ruellas, Brent Larson, Luis Enersto Arriola Guillén, Yalil Augusto Rodríguez-Cárdenas, Gustavo Armando Ruíz-Mora and 17 more

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in Clinical oral investigations, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

27 authors.

Sara TinawiDepartment of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA.
Rodrigo TeixeiraDepartment of Orthodontics, Bauru Dental School University of São Paulo, Alameda Dr. Octávio Pinheiro Brisolla, 9-75 ZIP Code, Bauru, 17012-901, SP, Brazil. rodrigoteixeira@usp.br.
Aron Aliaga-Del CastilloDepartment of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA.
Erika BenavidesDepartment of Periodontics and Oral Medicine, School of Dentistry, University Michigan, Ann Arbor, MI, USA.
Fabiana SokiDepartment of Periodontics and Oral Medicine, School of Dentistry, University Michigan, Ann Arbor, MI, USA.
Antonio Carlos De Oliveira RuellasDepartment of Orthodontics, School of Dentistry, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
Brent LarsonDivision of Orthodontics, School of Dentistry, University of Minnesota, Minneapolis, MN, USA.
Luis Enersto Arriola GuillénDivision of Orthodontics and Division of Oral and Maxillofacial Radiology, School of Dentistry, Universidad Científica del Sur, Lima, Perú.
Yalil Augusto Rodríguez-CárdenasDivision of Oral and Maxillofacial Radiology, School of Dentistry, Universidad Nacional de Colombia, Bogotá D.C, Bogotá, Colombia.
Gustavo Armando Ruíz-MoraDivision of Orthodontics, School of Dentistry, Universidad Nacional de Colombia, Bogota D.C, Colombia.
Jonas BianchiDepartment of Orthodontics, Arthur Dugoni School of Dentistry, University of the Pacific, San Francisco, CA, United States of America.
Marcela GurgelDepartment of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA.
Felicia MirandaDepartment of Orthodontics, Bauru Dental School and Hospital of Rehabilitation of Craniofacial Anomalies University of São Paulo, Bauru, SP, Brazil.
Dylan KeenerDepartment of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA.
Maxime GillotDepartment of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA.
Luc AnchlingDepartment of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA.
Jeanne ClaretDepartment of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA.
Christopher RobertsDepartment of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA.
Laurie McClatchey McNamaraDepartment of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA.
Mary BarkleyDepartment of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA.
Eric HannapelDepartment of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA.
Maria PinzonDepartment of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA.
Thomas GebeckDepartment of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA.
Nicholas ShireyDepartment of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA.
Claudia MattosDepartment of Orthodontics, School of Dentistry, Universidade Federal Fluminense, Niterói, Brazil.
Gregory HummonDepartment of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA.
Lucia CevidanesAdams School of Dentistry, University of North Caroline, Chapel Hill, NC, USA.

Funding

Quantification of 3D Bony Changes in Temporomandibular Joint OsteoarthritisR01DE024450 · NIDCR · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Lucia H Cevidanes · 2013 to 2026
$5.5M
NIDCR NIH HHS R01DE024450
6 · The paper itself

Abstract

introductionThis study aimed to validate an artificial intelligence (AI)-based automated image analysis for three-dimensional (3D) characterization of impacted canine position. In addition, it compared clinical treatment plans developed using conventional orthodontic records versus with the aid of the AI-generated diagnostic report.

methodsA sample of 228 cone-beam computed tomography (CBCT) scans for patients with impacted maxillary canines was retrospectively collected. AI models were used to automatically orient scans and identify anatomical landmarks to quantify impaction severity. The accuracy of the AI approach was validated by comparing its assessments with manual measurements performed by expert clinicians. Clinical applicability was assessed by comparing diagnoses made by clinicians using conventional records to those made using AI-based diagnostic system.

resultsA total of 316 impacted canines were assessed, with 20.9% classified as buccal, 31.3% as bicortical, and 47.8% as palatal. The AI-based diagnostic system accurately identified the position of impacted canines, achieving a mean detection success rate of 96.2%. Palatally impacted canines tend to have the crown closer to the midpalatal plane, reduced vertical distance to the occlusal plane, and greater pitch and roll angles compared to bicortically and buccally impacted canines (P<0.001). Overlap with adjacent teeth was the primary contributor to the AI-generated severity index (81.11%). The AI-based diagnostic system report significantly influenced clinicians' decisions, particularly patient education (72.31%) and biomechanics (51.15%).

conclusionsThe AI-based diagnostic system accurately characterized impacted canine position and severity, demonstrating the effectiveness of AI in enhancing diagnosis for impacted canine. CLINICAL RELEVANCE: The automated diagnostic report provides objective measurements that enhance clinicians' understanding of impaction complexity, improve patient communication, and support more informed biomechanical and treatment-planning decisions.

Indexed as

Artificial IntelligenceCone-Beam Computed TomographyCuspidImaging, Three-DimensionalMaxillaTooth, ImpactedAdolescentAdultFemaleHumansMaleRetrospective StudiesArtificial intelligenceCone-Beam computed tomographyDiagnostic imagingOrthodonticsTooth, impacted

Identifiers

PMID41222734

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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.