ArticleScientific reports2021
Age-group determination of living individuals using first molar images based on artificial intelligence.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT05901857 (Assessing the Precision of Convolutional Neural Networks for Dental Age Estimation in an Egyptian Population From Digital Panoramic Radiographs), which is not on this map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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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.
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.
Assessing the Precision of Convolutional Neural Networks for Dental Age Estimation in an Egyptian Population From Digital Panoramic Radiographs: A Diagnostic Accuracy Study
Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it, 89 citations in OpenAlex.
- A systematic overview of dental methods for age assessment in living individuals: from traditional to artificial intelligence-based approaches.International journal of legal medicine · 2023Pooled it
- Hybrid approach with convolutional neural network and regression models for age estimation in panoramic radiographs.International journal of legal medicine · 2026Observational
- Improving Chronological Age Estimation in Children Using the Demirjian Method Enhanced with Transformer and Regression Models.Journal of imaging informatics in medicine · 2026Article
- Automated Dental Age Estimation from Panoramic Radiographs using an Interpretable Deep Learning Model.International journal of legal medicine · 2026Article
- Deep learning-based dental age classification from panoramic radiographs: a multi-granularity, multi-model comparative study.BMC oral health · 2026Article
- Dental Age-Group Classification from Panoramic Radiographs Using Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2026Article
- Dental age estimation: a scoping review comparing the manual application of the Demirjian method and artificial intelligence modalities.International journal of legal medicine · 2026Article
- Evaluation of the diagnostic reliability of an AI prototype in detecting clinical features from dental photographs-original research.Frontiers in dental medicine · 2026Article
- Deep learning-based method for estimating age from periapical radiographs of upper incisors in a Thai population.Imaging science in dentistry · 2025Article
- Incisor pulp chamber tomographic image dataset for dental age estimation.Data in brief · 2025Article
- Comparison of different dental age estimation methods with deep learning: Willems, Cameriere-European, London Atlas.International journal of legal medicine · 2025Article
- Deep learning for forensic age estimation using orthopantomograms in children, adolescents, and young adults.European radiology · 2025Article
- Dental age estimation using a convolutional neural network algorithm on panoramic radiographs: A pilot study in Indonesia.Imaging science in dentistry · 2025Article
- Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic-Random Forest.Diagnostics (Basel, Switzerland) · 2025Article
- Determination of sexual dimorphism with CBCT images of the frontal sinus using a predictive formula and an artificial neural network.Journal of applied oral science : revista FOB · 2025Article
- Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.Clinical and experimental dental research · 2024Article
- Estimation of human age using machine learning on panoramic radiographs for Brazilian patients.Scientific reports · 2024Article
- Automatic prediction of obstructive sleep apnea in patients with temporomandibular disorder based on multidata and machine learning.Scientific reports · 2024Article
- Performance of Artificial Intelligence Models Designed for Automated Estimation of Age Using Dento-Maxillofacial Radiographs-A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Machine Learning Supported the Modified Gustafson's Criteria for Dental Age Estimation in Southwest China.Journal of imaging informatics in medicine · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dental age estimation of living individuals is difficult and challenging, and there is no consensus method in adults with permanent dentition. Thus, we aimed to provide an accurate and robust artificial intelligence (AI)-based diagnostic system for age-group estimation by incorporating a convolutional neural network (CNN) using dental X-ray image patches of the first molars extracted via panoramic radiography. The data set consisted of four first molar images from the right and left sides of the maxilla and mandible of each of 1586 individuals across all age groups, which were extracted from their panoramic radiographs. The accuracy of the tooth-wise estimation was 89.05 to 90.27%. Performance accuracy was evaluated mainly using a majority voting system and area under curve (AUC) scores. The AUC scores ranged from 0.94 to 0.98 for all age groups, which indicates outstanding capacity. The learned features of CNNs were visualized as a heatmap, and revealed that CNNs focus on differentiated anatomical parameters, including tooth pulp, alveolar bone level, or interdental space, depending on the age and location of the tooth. With this, we provided a deeper understanding of the most informative regions distinguished by age groups. The prediction accuracy and heat map analyses support that this AI-based age-group determination model is plausible and useful.
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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.