Evidence map›Paper›PMID 42585456›Full record

Observational studyDental press journal of orthodontics2026

Association between tooth agenesis and developmental dental anomalies: an observational study with machine learning analysis.

Guilherme Henrique Borges, Walbert A Vieira, Isabela Vinhal Henriques, Lígia Machado Silva, Rubens Spin-Neto, Marcos Alan Vieira Bittencourt, Luiz Renato Paranhos

Abstract readObservational Study
In one paragraph

Observational study in Dental press journal of orthodontics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Guilherme Henrique BorgesUniversidade Federal de Uberlândia, School of Dentistry (Uberlândia/MG, Brazil).ORCID http://orcid.org/0000-0003-4086-0534
Walbert A VieiraCentro Universitário das Faculdades Associadas de Ensino, School of Dentistry (São João da Boa Vista/SP, Brazil).ORCID http://orcid.org/0000-0001-8872-2865
Isabela Vinhal HenriquesUniversidade Federal de Uberlândia, School of Dentistry (Uberlândia/MG, Brazil).ORCID http://orcid.org/0009-0000-8153-2532
Lígia Machado SilvaUniversidade Federal de Uberlândia, School of Dentistry (Uberlândia/MG, Brazil).ORCID http://orcid.org/0009-0001-1384-2798
Rubens Spin-NetoAarhus University, Department of Dentistry and Oral Health - Division of Oral Radiology and Endodontics (Aarhus, Denmark).ORCID http://orcid.org/0000-0002-9562-0610
Marcos Alan Vieira BittencourtUniversidade Federal da Bahia, School of Dentistry, Department of Social and Pediatric Dentistry (Salvador/BA, Brazil).ORCID http://orcid.org/0000-0003-1728-2414
Luiz Renato ParanhosUniversidade Federal de Uberlândia, School of Dentistry, Department of Orthodontics (Uberlândia/MG, Brazil).ORCID http://orcid.org/0000-0002-7599-0120

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo investigate the prevalence of tooth agenesis and its association with other developmental dental anomalies in non-syndromic patients. The study also evaluated the applicability of machine learning models to predict the occurrence of agenesis.

methodsThis cross-sectional observational study analyzed 4,990 panoramic radiographs from radiology centers in Uberlândia, Minas Gerais/Brazil. It assessed the presence of tooth agenesis and associated anomalies, such as palatal displacement of canines, distoangulation of lower second premolars, tooth transposition, infraocclusion of deciduous molars, mesioangulation of lower second molars, and supernumerary teeth. The tooth agenesis code (TAC) mapped the patterns of occurrence. Fisher's exact test, Student's t-test, and logistic regression were used to analyze the data (p < 0.05). The machine learning models used included Decision Tree, Random Forest, XGBoost, Support Vector Machine, and Deep Learning.

resultsAgenesis was found in 6.1% of patients, with a higher prevalence in females (57.6%) and younger individuals (mean age of 16.8 years). Dental transposition (OR: 19.11; p < 0.001), maxillary canine displacement (OR: 2.08; p < 0.001), and deciduous molar infraocclusion (OR: 147.73; p < 0.001) showed a significant association. The Random Forest model showed the best predictive performance, but with clinical limitations.

conclusionTooth agenesis is associated with an increased risk of other anomalies, highlighting the importance of early diagnosis and integrated therapeutic approaches.

Indexed as

AnodontiaMachine LearningAdolescentBoosting Machine Learning AlgorithmsBrazilChildClassification AlgorithmsCross-Sectional StudiesFemaleHumansMalePredictive Learning ModelsPrevalenceRadiography, PanoramicRandom Forest

Identifiers

PMID42585456
PMCPMC13450905

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Registered trials

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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.