Application of Convolutional Neural Networks for Determining Gender and Age in Forensic Dentistry
Notice bibliographique
Résumé
Background Determining human identity has always been important in forensic investigations. Forensic dentistry has developed significantly having a key role in determining gender and age. One of the methods that is important in forensic dentistry is the analysis of orthopantomograms, which are X-rays of the complete upper and lower jaw, including the surrounding anatomical structures. The uniqueness of the dental features recorded in orthopantomograms makes them useful for individual identification, more specifically for the assessment of gender and age. This study was conducted to evaluate the application of convolutional neural networks in automating the process of gender and age estimation based on orthopantomograms, to improve accuracy and efficiency in forensic dentistry. Methodology Convolutional neural networks are powerful tools in the field of artificial intelligence for image processing and analysis because their convolutional layers extract specific features that are characteristic of a certain class. A total of 3716 orthopantomograms collected from the database of the University of Sarajevo - Faculty of Dentistry with the Dental Clinical Center were used to create convolutional neural network models for predicting gender and age. The orthopantomograms were taken in the period from January to December 2022 for the needs of doctors and providing services to patients at four polyclinics: Clinic for Dental Diseases and Endodontics, Clinic for Oral Diseases and Periodontology, Clinic for Oral Surgery, and Clinic for Pediatric and Preventive Dentistry. Results The results derived from three developed models confirm that the developed convolutional neural networks have high accuracy. The first model estimated gender, while the second and the third models estimated age within certain age ranges, the second from 12 to 24 years, and the third from 20 to 70 years. After training on the training dataset, all models achieved high accuracy on the validation dataset. The models demonstrated high accuracy without signs of overfitting, with the first model achieving 95.98%, the second model achieving 97.90%, and the third model achieving 96.12% accuracy. Conclusion This research concluded that the developed convolutional neural networks for gender and age estimation from orthopantomograms showed high accuracy. Models' predictions of gender and two age groups exceeded 95% accuracy. Therefore, convolutional neural networks can be considered useful tools for gender and age determination in forensic dentistry and can facilitate and speed up the processes of assessment and determination of essential characteristics.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».