ArticleScientific reports2022
Diagnostic performance of convolutional neural networks for dental sexual dimorphism.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review and Meta-Analysis on the Accuracy of Artificial Intelligence Algorithms in Individuals Gender Detection Using Orthopantomograms.International dental journal · 2025Pooled it
- Computed tomography as a tool in the analysis of dentomaxillofacial characteristics for human identification: A bibliometric analysis of the most cited publications.Forensic science, medicine, and pathology · 2026Article
- Artificial Intelligence-Assisted Sex Estimation from Canine Measurements on Panoramic Radiographs: Comparison with Manual Analysis in a Romanian Sample.Diagnostics (Basel, Switzerland) · 2026Article
- A Pilot Study on AI-Driven Age Estimation and Sex Determination in Greek Individuals.Journal of imaging · 2026Article
- Comparative Analysis of Third Molar Segmentation Performance Between Sexes Using Deep Learning Models.Diagnostics (Basel, Switzerland) · 2026Article
- Article
- Maxillary sinus classification for sex and age using 23 artificial intelligence architectures.Scientific reports · 2026Article
- YOLO11m-cls applied to sex and age classification based on the radiographic analysis of the nasal aperture.Scientific reports · 2025Article
- Development and evaluation of a convolutional neural network model for sex prediction using cephalometric radiographs and cranial photographs.BMC medical imaging · 2025Article
- Article
- The role of artificial intelligence in diagnosing pediatric dental disorders-a narrative review.Frontiers in dental medicine · 2025Review
- Sex determination through maxillary dental arch and skeletal base measurements using machine learning.Head & face medicine · 2024Article
- Classification of Indonesian adult forensic gender using cephalometric radiography with VGG16 and VGG19: a Preliminary research.Acta odontologica Scandinavica · 2024Article
- Binary decisions of artificial intelligence to classify third molar development around the legal age thresholds of 14, 16 and 18 years.Scientific reports · 2024Article
- Accuracy of an AI-based automated plate reading mobile application for the identification of clinical mastitis-causing pathogens in chromogenic culture media.Scientific reports · 2024Article
- Artificial intelligence model for predicting sexual dimorphism through the hyoid bone in adult patients.PloS one · 2024Article
- Teeth Microcracks Research: Towards Multi-Modal Imaging.Bioengineering (Basel, Switzerland) · 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
- Applications of contemporary artificial intelligence technology in forensic odontology as primary forensic identifier: A scoping review.Frontiers in artificial intelligence · 2022Article
Corrections and comments
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Authors and funding
12 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Convolutional neural networks (CNN) led to important solutions in the field of Computer Vision. More recently, forensic sciences benefited from the resources of artificial intelligence, especially in procedures that normally require operator-dependent steps. Forensic tools for sexual dimorphism based on morphological dental traits are available but have limited performance. This study aimed to test the application of a machine learning setup to distinguish females and males using dentomaxillofacial features from a radiographic dataset. The sample consisted of panoramic radiographs (n = 4003) of individuals in the age interval of 6 and 22.9 years. Image annotation was performed with V7 software (V7labs, London, UK). From Scratch (FS) and Transfer Learning (TL) CNN architectures were compared, and diagnostic accuracy tests were used. TL (82%) performed better than FS (71%). The correct classifications of females and males aged ≥ 15 years were 87% and 84%, respectively. For females and males < 15 years, the correct classifications were 80% and 83%, respectively. The Area Under the Curve (AUC) from Receiver-operating Characteristic (ROC) curves showed high classification accuracy between 0.87 and 0.91. The radio-diagnostic use of CNN for sexual dimorphism showed positive outcomes and promising forensic applications to the field of dental human identification.
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