ArticleJournal of imaging2023
Age Assessment through Root Lengths of Mandibular Second and Third Permanent Molars Using Machine Learning and Artificial Neural Networks.
Article in Journal of imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 29 citations in OpenAlex.
- Hybrid approach with convolutional neural network and regression models for age estimation in panoramic radiographs.International journal of legal medicine · 2026Observational
- Application of a novel age estimation model based on permanent maxillary canine morphometric features in a Japanese population.Oral radiology · 2026Article
- Clinically interpretable deep learning for pediatric dental age estimation with explainable AI.BMC oral health · 2026Article
- Age estimation of children and adolescents from mandibles using machine learning.Scientific reports · 2025Article
- Feasibility Study on Optimising the Efficacy of a Population Age Estimation Model for South China by Combined Machine Learning for the Second and Third Molars.Journal of imaging informatics in medicine · 2025Article
- Age estimation by volumetric analysis of teeth using cone beam computed tomography.Oral radiology · 2024Article
- ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision tree.Frontiers in genetics · 2023Article
- A Nonlinear Three-Dimensional Finite Element Analysis of Stress Distribution and Microstrain Evaluation in Short Dental Implants with Three Different Implant-Abutment Connections in Single and Splinted Conditions in the Posterior Mandible.International journal of dentistry · 2023Article
- Reliability of artificial intelligence algorithms in automated age estimation using orthopantomograms: A scoping review.Digital healthReview
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The present study explores the efficacy of Machine Learning and Artificial Neural Networks in age assessment using the root length of the second and third molar teeth. A dataset of 1000 panoramic radiographs with intact second and third molars ranging from 12 to 25 years was archived. The length of the mesial and distal roots was measured using ImageJ software. The dataset was classified in three ways based on the age distribution: 2-Class, 3-Class, and 5-Class. We used Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression models to train, test, and analyze the root length measurements. The mesial root of the third molar on the right side was a good predictor of age. The SVM showed the highest accuracy of 86.4% for 2-class, 66% for 3-class, and 42.8% for 5-Class. The RF showed the highest accuracy of 47.6% for 5-Class. Overall the present study demonstrated that the Deep Learning model (fully connected model) performed better than the Machine Learning models, and the mesial root length of the right third molar was a good predictor of age. Additionally, a combination of different root lengths could be informative while building a Machine Learning model.
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Registered trials
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.