Estimation of human age using machine learning on panoramic radiographs for Brazilian patients.
Sci Rep
; 14(1): 19689, 2024 08 24.
Article
de En
| MEDLINE
| ID: mdl-39181957
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.
Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Détermination de l'âge dentaire
/
Radiographie panoramique
/
Apprentissage machine
Limites:
Adolescent
/
Adult
/
Aged
/
Aged80
/
Child
/
Child, preschool
/
Female
/
Humans
/
Male
/
Middle aged
Pays/Région comme sujet:
America do sul
/
Brasil
Langue:
En
Journal:
Sci Rep
/
Sci. rep. (Nat. Publ. Group)
/
Scientific reports (Nature Publishing Group)
Année:
2024
Type de document:
Article
Pays d'affiliation:
Brésil
Pays de publication:
Royaume-Uni