ArticleBMC bioinformatics2022
DENSEN: a convolutional neural network for estimating chronological ages from panoramic radiographs.
Article in BMC bioinformatics, 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 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in age and sex determination using maxillofacial radiographs: A systematic review.The Journal of forensic odonto-stomatology · 2024Pooled it
- Panoramic imaging errors in machine learning model development: a systematic review.Dento maxillo facial radiology · 2024Pooled 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
- Deep learning-based dental age estimation in adolescents using panoramic radiographs: a comparative study with traditional methods.Frontiers in radiology · 2026Article
- Dental age prediction from panoramic radiographs using machine learning techniques.PLOS digital health · 2025Article
- The impact of multi-modality fusion and deep learning on adult age estimation based on bone mineral density.International journal of legal medicine · 2025Article
- Deep learning for forensic age estimation using orthopantomograms in children, adolescents, and young adults.European radiology · 2025Article
- Performance of Artificial Intelligence Models Designed for Automated Estimation of Age Using Dento-Maxillofacial Radiographs-A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Estimating chronological age through learning local and global features of panoramic radiographs in the Korean population.Scientific reports · 2023Article
- Artificial intelligence in forensic medicine and forensic dentistry.The Journal of forensic odonto-stomatology · 2023Review
- A population-based study to assess two convolutional neural networks for dental age estimation.BMC oral health · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
Abstract
backgroundAge estimation from panoramic radiographs is a fundamental task in forensic sciences. Previous age assessment studies mainly focused on juvenile rather than elderly populations (> 25 years old). Most proposed studies were statistical or scoring-based, requiring wet-lab experiments and professional skills, and suffering from low reliability.
resultBased on Soft Stagewise Regression Network (SSR-Net), we developed DENSEN to estimate the chronological age for both juvenile and older adults, based on their orthopantomograms (OPTs, also known as orthopantomographs, pantomograms, or panoramic radiographs). We collected 1903 clinical panoramic radiographs of individuals between 3 and 85 years old to train and validate the model. We evaluated the model by the mean absolute error (MAE) between the estimated age and ground truth. For different age groups, 3-11 (children), 12-18 (teens), 19-25 (young adults), and 25+ (adults), DENSEN produced MAEs as 0.6885, 0.7615, 1.3502, and 2.8770, respectively. Our results imply that the model works in situations where genders are unknown. Moreover, DENSEN has lower errors for the adult group (> 25 years) than other methods. The proposed model is memory compact, consuming about 1.0 MB of memory overhead.
conclusionsWe introduced a novel deep learning approach DENSEN to estimate a subject's age from a panoramic radiograph for the first time. Our approach required less laboratory work compared with existing methods. The package we developed is an open-source tool and applies to all different age groups.
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