Evidence map›Paper›PMID 40819046›Full record

ArticleBMC oral health2025

A hybrid convolutional neural network model for dental age estimation using buccal alveolar bone level for Saudi children.

Seyed Matin Mazloom Nezhad, Erma Rahayu Mohd Faizal Abdullah, Norliza Ibrahim, Heba H Bakhsh, Uzair Ishtiaq, Rabiah Al Adawiyah Rahmat, Sarah AlMugairin, Sara M ElKhateeb

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC oral health, 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

8 authors.

Seyed Matin Mazloom NezhadDepartment of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, 50603, Malaysia. matin@um.edu.my.
Erma Rahayu Mohd Faizal AbdullahDepartment of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, 50603, Malaysia. erma@um.edu.my.
Norliza IbrahimDepartment of Oral and Maxillofacial Clinical Sciences, Faculty of Dentistry, Universiti Malaya, Kuala Lumpur, 50603, Malaysia. norlizaibrahim@um.edu.my.
Heba H BakhshDepartment of Preventive Dental Sciences, College of Dentistry, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Uzair IshtiaqDepartment of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, 50603, Malaysia.
Rabiah Al Adawiyah RahmatDepartment of Oral and Maxillofacial Clinical Sciences, Faculty of Dentistry, Universiti Malaya, Kuala Lumpur, 50603, Malaysia.
Sarah AlMugairinDepartment of Preventive Dental Sciences, College of Dentistry, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Sara M ElKhateebDepartment of Basic Dental Sciences, College of Dentistry, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.

Funding

Faculty of Dentistry, Universiti Malaya UM.0000371/KWJ.SRFundamental Research Grant Scheme (FRGS), Ministry of Higher Education, Malaysia FP1212020/FRGS/1/2020/SKK0/UM/02/23
6 · The paper itself

Abstract

backgroundAge estimation is an essential task in medical dentistry and forensic sciences. Dental Age Estimation (DAE) is one of the most common methods for age estimation. Teeth are commonly used for age estimation because the schedules of tooth development and eruption are barely affected by the environment, nutrition, and socio-economic factors. However, conventional DAE methods are manually performed by clinicians, exposing bias and error to the estimated age. Moreover, the potentials of buccal alveolar bone level in DAE are rarely investigated. The aim of this study is to assess the effectiveness of buccal alveolar bone level of mandibular posterior teeth in DAE using Artificial Intelligence (AI) for children.

methodsA total of 421 Dental Panoramic Tomography (DPT) of children ranging from 5 to 15 years of age were used to train multiple UNet segmentation models. Segmented images of teeth were extracted and fed into a Localization Convolutional Neural Network (CNN) to train them for measuring buccal alveolar bone level. Moreover, the buccal alveolar bone level measurements were then fed to the machine learning regression models for DAE.

resultThe transfer learning based UNet with VGG16 as its backbone achieved the best performance with an IoU score of 0.66 and the best performing Localization CNN achieved Mean Squared Error (MSE) of 0.0009 and [Formula: see text] score of 0.8266. The Support Vector Machine (SVM) regression model achieved the best mean absolute error of 0.99 year.

conclusionThe results revealed the potential of buccal alveolar bone level for dental age estimation in children. Best performing model achieved an acceptable Mean Absolute Error (MAE) and similar results to Demirjian and London Atlas methods performed by human experts, showing promising results.

trial registrationNot applicable.

Indexed as

Age Determination by TeethAlveolar ProcessConvolutional Neural NetworksAdolescentChildChild, PreschoolFemaleHumansMachine LearningMaleMandibleNeural Networks, ComputerRadiography, PanoramicSaudi ArabiaAge estimationBuccal alveolar bone levelDeep learningDental ageMachine learningSupport vector machine

Identifiers

PMID40819046
PMCPMC12357355

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.