ArticleInternational journal of legal medicine2022
With or without human interference for precise age estimation based on machine learning?
Article in International journal of legal medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 21 papers, 3 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Dental age estimation in forensic odontology: a systematic review comparing traditional methods and artificial intelligence approaches.Frontiers in dental medicine · 2026Pooled it
- Efficacy of the methods of age determination using artificial intelligence in panoramic radiographs - a systematic review.International journal of legal medicine · 2024Pooled it
- Deep learning methods for fully automated dental age estimation on orthopantomograms.Clinical oral investigations · 2024Pooled it
- Dual-input deep learning bone age assessment in late adolescence using hand and knee radiographs.Skeletal radiology · 2026Article
- Automated Dental Age Estimation from Panoramic Radiographs using an Interpretable Deep Learning Model.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
- Radiographic legal age estimation based on third molar development using Machine-learning algorithms.PloS one · 2026Article
- Development of an age estimation method for the coxal bone and lumbar vertebrae obtained from post-mortem computed tomography images using a convolutional neural network.International journal of legal medicine · 2026Article
- End-to-end vs. human-defined feature extraction: comparing deep learning approaches for age classification using mandibular third molars.The Journal of forensic odonto-stomatology · 2025Article
- An autoencoder and vision transformer based interpretability analysis on the performance differences in automated staging of second and third molars.Scientific reports · 2025Article
- Incisor pulp chamber tomographic image dataset for dental age estimation.Data in brief · 2025Article
- Deep learning for forensic age estimation using orthopantomograms in children, adolescents, and young adults.European radiology · 2025Article
- Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic-Random Forest.Diagnostics (Basel, Switzerland) · 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
- Artificial intelligence and dental age estimation: development and validation of an automated stage allocation technique on all mandibular tooth types in panoramic radiographs.International journal of legal medicine · 2024Article
- 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
- Semi-supervised automatic dental age and sex estimation using a hybrid transformer model.International journal of legal medicine · 2023Article
- Variational autoencoder-based estimation of chronological age and changes in morphological features of teeth.Scientific reports · 2023Article
- Validation of data mining models by comparing with conventional methods for dental age estimation in Korean juveniles and young adults.Scientific reports · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
12 authors.
Funding
Abstract
Age estimation can aid in forensic medicine applications, diagnosis, and treatment planning for orthodontics and pediatrics. Existing dental age estimation methods rely heavily on specialized knowledge and are highly subjective, wasting time, and energy, which can be perfectly solved by machine learning techniques. As the key factor affecting the performance of machine learning models, there are usually two methods for feature extraction: human interference and autonomous extraction without human interference. However, previous studies have rarely applied these two methods for feature extraction in the same image analysis task. Herein, we present two types of convolutional neural networks (CNNs) for dental age estimation. One is an automated dental stage evaluation model (ADSE model) based on specified manually defined features, and the other is an automated end-to-end dental age estimation model (ADAE model), which autonomously extracts potential features for dental age estimation. Although the mean absolute error (MAE) of the ADSE model for stage classification is 0.17 stages, its accuracy in dental age estimation is unsatisfactory, with the MAE (1.63 years) being only 0.04 years lower than the manual dental age estimation method (MDAE model). However, the MAE of the ADAE model is 0.83 years, being reduced by half that of the MDAE model. The results show that fully automated feature extraction in a deep learning model without human interference performs better in dental age estimation, prominently increasing the accuracy and objectivity. This indicates that without human interference, machine learning may perform better in the application of medical imaging.
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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.