ArticleScientific reports2024
Estimation of human age using machine learning on panoramic radiographs for Brazilian patients.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- The influence of adjacent anatomical context in tooth staging: observational study of non-cropped vs. cropped radiographs for dental age estimation.International journal of legal medicine · 2026Observational
- Age estimation through mandibular radiomic attributes in panoramic radiographs using convolutional neural networks.Imaging science in dentistry · 2026Article
- Forensic dental age estimation in a Pakistani population: validation of Atlas methods and development of a population-specific reference model.BMC oral health · 2026Article
- Clinically interpretable deep learning for pediatric dental age estimation with explainable AI.BMC oral health · 2026Article
- Accuracy and challenges in age estimation in adults: a scoping review of anthropological, dental, biochemical, and molecular methods.Forensic science, medicine, and pathology · 2025Article
- Recognition model for counterfeit protection system in colour-laser-printed documents based on improved ShuffleNet V2.Scientific reports · 2025Article
- Age Estimation from Lateral Cephalograms Using Deep Learning: A Pilot Study from Early Childhood to Older Adults.Journal of clinical medicine · 2025Article
- [An lightweight algorithm for multi-dimensional optimization of intelligent detection of dental abnormalities on panoramic oral X-ray images].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic-Random Forest.Diagnostics (Basel, Switzerland) · 2025Article
- An Artificial Intelligence Pipeline for Hepatocellular Carcinoma: From Data to Treatment Recommendations.International journal of general medicine · 2025Review
- Reliability of artificial intelligence algorithms in automated age estimation using orthopantomograms: A scoping review.Digital healthReview
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper addresses a relevant problem in Forensic Sciences by integrating radiological techniques with advanced machine learning methodologies to create a non-invasive, efficient, and less examiner-dependent approach to age estimation. Our study includes a new dataset of 12,827 dental panoramic X-ray images representing the Brazilian population, covering an age range from 2.25 to 96.50 years. To analyze these exams, we employed a model adapted from InceptionV4, enhanced with data augmentation techniques. The proposed approach achieved robust and reliable results, with a Test Mean Absolute Error of 3.1 years and an R-squared value of 95.5%. Professional radiologists have validated that our model focuses on critical features for age assessment used in odontology, such as pulp chamber dimensions and stages of permanent teeth calcification. Importantly, the model also relies on anatomical information from the mandible, maxillary sinus, and vertebrae, which enables it to perform well even in edentulous cases. This study demonstrates the significant potential of machine learning to revolutionize age estimation in Forensic Science, offering a more accurate, efficient, and universally applicable solution.
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