Dental Age Estimation Using Deep Learning: A Comparative Survey

Dental Age Estimation Using Deep Learning: A Comparative Survey

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...

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Bibliographic Details
Published in:Computation Vol. 11; no. 2; p. 18
Main Authors: Mohamed, Essraa Gamal, Redondo, Rebeca P. Díaz, Koura, Abdelrahim, EL-Mofty, Mohamed Sherif, Kayed, Mohammed
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-01-2023
Subjects:
Accuracy
Age
Age determination (Zoology)
Aging
Artificial neural networks
Automation
Biometrics
Biometry
Bones
Cataracts
Chronology
Computed tomography
convolutional neural networks
Deep learning
dental age estimation
Gender
Geometry
Glaucoma
Machine learning
Neural networks
orthopantomography
Physiological aspects
Physiology
Teeth
Tomography
Undocumented immigrants
Vertebrae
X-rays
Egypt
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Summary: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.
ISSN:2079-3197
2079-3197
DOI:10.3390/computation11020018