RT Journal Article
T1 Dental age estimation using deep learning: a comparative survey
A1 Mohamed, Essraa Gamal
A1 Díaz Redondo, Rebeca Pilar
A1 Koura, Abdelrahim
A1 EL-Mofty, Mohamed Sherif
A1 Kayed, Mohammed
K1 3314 Tecnología Médica
K1 1209.03 Análisis de Datos
K1 1203.04 Inteligencia Artificial
AB The significance of age estimation arises from its applications in various fields, such as forensics, criminal investigation, and illegal immigration. Due to the increased importance of age estimation, this area of study requires more investigation and development. Several methods for age estimation using biometrics traits, such as the face, teeth, bones, and voice. Among then, teeth are quite convenient since they are resistant and durable and are subject to several changes from childhood to birth that can be used to derive age. In this paper, we summarize the common biometrics traits for age estimation and how this information has been used in previous research studies for age estimation. We have paid special attention to traditional machine learning methods and deep learning approaches used for dental age estimation. Thus, we summarized the advances in convolutional neural network (CNN) models to estimate dental age from radiological images, such as 3D cone-beam computed tomography (CBCT), X-ray, and orthopantomography (OPG) to estimate dental age. Finally, we also point out the main innovations that would potentially increase the performance of age estimation systems.
PB Computation
SN 20793197
YR 2023
FD 2023-01-29
LK http://hdl.handle.net/11093/4474
UL http://hdl.handle.net/11093/4474
LA eng
NO Computation, 11(2): 18 (2023)
NO Agencia Estatal de Investigación | Ref. PID2020-113795RB-C33
DS Investigo
RD 19-abr-2025