Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters
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 de...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/2/637 |
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| author | Maciej Zaborowicz Katarzyna Zaborowicz Barbara Biedziak Tomasz Garbowski |
| author_facet | Maciej Zaborowicz Katarzyna Zaborowicz Barbara Biedziak Tomasz Garbowski |
| author_sort | Maciej Zaborowicz |
| collection | DOAJ |
| description | 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 R<sup>2</sup> ranged from 0.92 to 0.96. |
| first_indexed | 2024-03-10T00:33:59Z |
| format | Article |
| id | doaj.art-f6a1d72db5b54304a83a46fefc6c490e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T00:33:59Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f6a1d72db5b54304a83a46fefc6c490e2023-11-23T15:21:59ZengMDPI AGSensors1424-82202022-01-0122263710.3390/s22020637Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone ParametersMaciej Zaborowicz0Katarzyna Zaborowicz1Barbara Biedziak2Tomasz Garbowski3Department of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-627 Poznan, PolandDepartment of Orthodontics and Craniofacial Anomalies, Poznan University of Medical Sciences, Collegium Maius, Fredry 10, 61-701 Poznan, PolandDepartment of Orthodontics and Craniofacial Anomalies, Poznan University of Medical Sciences, Collegium Maius, Fredry 10, 61-701 Poznan, PolandDepartment of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-627 Poznan, PolandDental 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 R<sup>2</sup> ranged from 0.92 to 0.96.https://www.mdpi.com/1424-8220/22/2/637chronological agedental ageage assessmentdigital pantomographydigital image analysisartificial intelligence |
| spellingShingle | Maciej Zaborowicz Katarzyna Zaborowicz Barbara Biedziak Tomasz Garbowski 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 chronological age dental age age assessment digital pantomography digital image analysis artificial intelligence |
| title | Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters |
| title_full | Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters |
| title_fullStr | Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters |
| title_full_unstemmed | Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters |
| title_short | Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters |
| title_sort | deep learning neural modelling as a precise method in the assessment of the chronological age of children and adolescents using tooth and bone parameters |
| topic | chronological age dental age age assessment digital pantomography digital image analysis artificial intelligence |
| url | https://www.mdpi.com/1424-8220/22/2/637 |
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