ArticleSensors (Basel, Switzerland)2022
Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 38 citations in OpenAlex.
- Application of a novel age estimation model based on permanent maxillary canine morphometric features in a Japanese population.Oral radiology · 2026Article
- 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
- Automated Cephalometric Points Marking System.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence Applications in Pediatric Dentistry: A Comprehensive Review.Biomimetics (Basel, Switzerland) · 2026Review
- A Lightweight and Explainable AI Framework Toward Automated Infraocclusion Detection in Pediatric Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Pediatric Dentistry: A Systematic Review and Meta-Analysis.Children (Basel, Switzerland) · 2026Review
- Deep learning-based dental age estimation in adolescents using panoramic radiographs: a comparative study with traditional methods.Frontiers in radiology · 2026Article
- Forensic dental age estimation with deep learning: a modified xception model for panoramic X-Ray images.Forensic science, medicine, and pathology · 2025Article
- Bone Age Assessment Using Various Medical Imaging Techniques Enhanced by Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2025Review
- The role of artificial intelligence in diagnosing pediatric dental disorders-a narrative review.Frontiers in dental medicine · 2025Review
- Tech Bytes-Harnessing Artificial Intelligence for Pediatric Oral Health: A Scoping Review.International journal of clinical pediatric dentistry · 2024Article
- Evaluation of tooth development stages with deep learning-based artificial intelligence algorithm.BMC oral health · 2024Article
- Review
- Application of Artificial Intelligence in Pediatric Dentistry: A Literature Review.Journal of pharmacy & bioallied sciences · 2024Review
- The Implications of Artificial Intelligence in Pedodontics: A Scoping Review of Evidence-Based Literature.Healthcare (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
- Experience of Using Neural Networks to Assess Age-Related Changes in Some Structures of the Skull and Cervical Vertebrae Based on CT Scans (Pilot Project).Sovremennye tekhnologii v meditsine · 2024Article
- Artificial intelligence in forensic medicine and forensic dentistry.The Journal of forensic odonto-stomatology · 2023Review
- Artificial Intelligence Its Uses and Application in Pediatric Dentistry: A Review.Biomedicines · 2023Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dental age is one of the most reliable methods for determining a patient's age. The timing of teething, the period of tooth replacement, or the degree of tooth attrition is an important diagnostic factor in the assessment of an individual's developmental age. It is used in orthodontics, pediatric dentistry, endocrinology, forensic medicine, and pathomorphology, but also in scenarios regarding international adoptions and illegal immigrants. The methods used to date are time-consuming and not very precise. For this reason, artificial intelligence methods are increasingly used to estimate the age of a patient. The present work is a continuation of the work of Zaborowicz et al. In the presented research, a set of 21 original indicators was used to create deep neural network models. The aim of this study was to verify the ability to generate a more accurate deep neural network model compared to models produced previously. The quality parameters of the produced models were as follows. The MAE error of the produced models, depending on the learning set used, was between 2.34 and 4.61 months, while the RMSE error was between 5.58 and 7.49 months. The correlation coefficient R
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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.