ArticleHealthcare (Basel, Switzerland)2023
Age Group Classification of Dental Radiography without Precise Age Information Using Convolutional Neural Networks.
Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- Improving Chronological Age Estimation in Children Using the Demirjian Method Enhanced with Transformer and Regression Models.Journal of imaging informatics in medicine · 2026Article
- Deep learning-based dental age classification from panoramic radiographs: a multi-granularity, multi-model comparative study.BMC oral health · 2026Article
- Deep learning-based dental age estimation in adolescents using panoramic radiographs: a comparative study with traditional methods.Frontiers in radiology · 2026Article
- Automatic dental age estimation in adolescents via oral panoramic imaging.Frontiers in dental medicine · 2025Article
- Artificial Intelligence for Tooth Detection in Cleft Lip and Palate Patients.Diagnostics (Basel, Switzerland) · 2024Article
- Performance of Artificial Intelligence Models Designed for Automated Estimation of Age Using Dento-Maxillofacial Radiographs-A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- 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
7 authors at 4 institutions in 1 country.
Funding
Abstract
Automatic age estimation using panoramic dental radiographic images is an important procedure for forensics and personal oral healthcare. The accuracies of the age estimation have increased recently with the advances in deep neural networks (DNN), but DNN requires large sizes of the labeled dataset which is not always available. This study examined whether a deep neural network is able to estimate tooth ages when precise age information is not given. A deep neural network model was developed and applied to age estimation using an image augmentation technique. A total of 10,023 original images were classified according to age groups (in decades, from the 10s to the 70s). The proposed model was validated using a 10-fold cross-validation technique for precise evaluation, and the accuracies of the predicted tooth ages were calculated by varying the tolerance. The accuracies were 53.846% with a tolerance of ±5 years, 95.121% with ±15 years, and 99.581% with ±25 years, which means the probability for the estimation error to be larger than one age group is 0.419%. The results indicate that artificial intelligence has potential not only in the forensic aspect but also in the clinical aspect of oral care.
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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.