Mohamed, Essraa Gamal and Redondo, Rebeca P. Díaz and Koura, Abdelrahim and EL-Mofty, Mohamed Sherif and Kayed, Mohammed (2023) Dental Age Estimation Using Deep Learning: A Comparative Survey. Computation, 11 (2). p. 18. ISSN 2079-3197
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Abstract
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.
Item Type: | Article |
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Subjects: | Middle East Library > Computer Science |
Depositing User: | Unnamed user with email support@middle-eastlibrary.com |
Date Deposited: | 01 Jun 2023 08:32 |
Last Modified: | 06 Sep 2024 09:04 |
URI: | http://editor.openaccessbook.com/id/eprint/980 |