Next Article in Journal
A TDMA-Based MAC Protocol for Mitigating Mobility-Caused Packet Collisions in Vehicular Ad Hoc Networks
Previous Article in Journal
Comprehensive Review on Wearable Sweat-Glucose Sensors for Continuous Glucose Monitoring
Article

Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters

1
Department of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-627 Poznan, Poland
2
Department of Orthodontics and Craniofacial Anomalies, Poznan University of Medical Sciences, Collegium Maius, Fredry 10, 61-701 Poznan, Poland
*
Authors to whom correspondence should be addressed.
Academic Editor: Krzysztof Kulpa
Sensors 2022, 22(2), 637; https://doi.org/10.3390/s22020637
Received: 20 December 2021 / Revised: 7 January 2022 / Accepted: 12 January 2022 / Published: 14 January 2022
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Poland 2021-2022)
Dental age is one of the most reliable methods for determining a patient’s age. The timing of teething, the period of tooth replacement, or the degree of tooth attrition is an important diagnostic factor in the assessment of an individual’s developmental age. It is used in orthodontics, pediatric dentistry, endocrinology, forensic medicine, and pathomorphology, but also in scenarios regarding international adoptions and illegal immigrants. The methods used to date are time-consuming and not very precise. For this reason, artificial intelligence methods are increasingly used to estimate the age of a patient. The present work is a continuation of the work of Zaborowicz et al. In the presented research, a set of 21 original indicators was used to create deep neural network models. The aim of this study was to verify the ability to generate a more accurate deep neural network model compared to models produced previously. The quality parameters of the produced models were as follows. The MAE error of the produced models, depending on the learning set used, was between 2.34 and 4.61 months, while the RMSE error was between 5.58 and 7.49 months. The correlation coefficient R2 ranged from 0.92 to 0.96. View Full-Text
Keywords: chronological age; dental age; age assessment; digital pantomography; digital image analysis; artificial intelligence; deep neural network chronological age; dental age; age assessment; digital pantomography; digital image analysis; artificial intelligence; deep neural network
Show Figures

Figure 1

MDPI and ACS Style

Zaborowicz, M.; Zaborowicz, K.; Biedziak, B.; Garbowski, T. Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters. Sensors 2022, 22, 637. https://doi.org/10.3390/s22020637

AMA Style

Zaborowicz M, Zaborowicz K, Biedziak B, Garbowski T. Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters. Sensors. 2022; 22(2):637. https://doi.org/10.3390/s22020637

Chicago/Turabian Style

Zaborowicz, Maciej, Katarzyna Zaborowicz, Barbara Biedziak, and Tomasz Garbowski. 2022. "Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters" Sensors 22, no. 2: 637. https://doi.org/10.3390/s22020637

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop