ReviewDiagnostics (Basel, Switzerland)2024
Performance of Artificial Intelligence Models Designed for Automated Estimation of Age Using Dento-Maxillofacial Radiographs-A Systematic Review.
Review in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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Who cites it
11 citing papers in PubMed.
- Comparative Analysis of Third Molar Segmentation Performance Across Dental Developmental Stages and the 18-Year Age Threshold Using Deep Learning Models.Diagnostics (Basel, Switzerland) · 2026Article
- Hybrid approach with convolutional neural network and regression models for age estimation in panoramic radiographs.International journal of legal medicine · 2026Article
- Dental age assessment: orthopantomogram-based comparison of traditional methods and artificial intelligence in the adult population.International journal of legal medicine · 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
- Artificial Intelligence in Dentistry: A Concise Review of Reporting Checklists and Guidelines.International dental journal · 2026Review
- Deep learning-based dental age estimation in adolescents using panoramic radiographs: a comparative study with traditional methods.Frontiers in radiology · 2026Article
- Radiographic legal age estimation based on third molar development using Machine-learning algorithms.PloS one · 2026Article
- Age Estimation from Lateral Cephalograms Using Deep Learning: A Pilot Study from Early Childhood to Older Adults.Journal of clinical medicine · 2025Article
- Development and evaluation of a deep learning-based system for dental age estimation using the demirjian method on panoramic radiographs.BMC oral health · 2025Article
- Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic-Random Forest.Diagnostics (Basel, Switzerland) · 2025Article
- Insights into dental age estimation: introducing multiple regression data from a Black South African population on modified gustafson's criteria.International journal of legal medicine · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automatic age estimation has garnered significant interest among researchers because of its potential practical uses. The current systematic review was undertaken to critically appraise developments and performance of AI models designed for automated estimation using dento-maxillofacial radiographic images. In order to ensure consistency in their approach, the researchers followed the diagnostic test accuracy guidelines outlined in PRISMA-DTA for this systematic review. They conducted an electronic search across various databases such as PubMed, Scopus, Embase, Cochrane, Web of Science, Google Scholar, and the Saudi Digital Library to identify relevant articles published between the years 2000 and 2024. A total of 26 articles that satisfied the inclusion criteria were subjected to a risk of bias assessment using QUADAS-2, which revealed a flawless risk of bias in both arms for the patient-selection domain. Additionally, the certainty of evidence was evaluated using the GRADE approach. AI technology has primarily been utilized for automated age estimation through tooth development stages, tooth and bone parameters, bone age measurements, and pulp-tooth ratio. The AI models employed in the studies achieved a remarkably high precision of 99.05% and accuracy of 99.98% in the age estimation for models using tooth development stages and bone age measurements, respectively. The application of AI as an additional diagnostic tool within the realm of age estimation demonstrates significant promise.
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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.