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

cris.virtual.author-orcid0000-0003-0000-6157
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cris.virtual.author-orcid0000-0002-9588-2514
cris.virtualsource.author-orcid5e10caab-6ff8-471e-83cf-04cdbe8885b6
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
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cris.virtualsource.author-orcidae71bc22-fde2-40b2-878c-e07e0e5aad5a
dc.abstract.enDental 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.
dc.affiliationWydział Inżynierii Środowiska i Inżynierii Mechanicznej
dc.affiliation.instituteKatedra Inżynierii Biosystemów
dc.contributor.authorZaborowicz, Maciej
dc.contributor.authorZaborowicz, Katarzyna
dc.contributor.authorBiedziak, Barbara
dc.contributor.authorGarbowski, Tomasz
dc.date.access2026-01-30
dc.date.accessioned2026-02-10T08:33:48Z
dc.date.available2026-02-10T08:33:48Z
dc.date.copyright2022-01-14
dc.date.issued2022
dc.description.abstract<jats:p>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.</jats:p>
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if3,9
dc.description.number2
dc.description.points100
dc.description.versionfinal_published
dc.description.volume22
dc.identifier.doi10.3390/s22020637
dc.identifier.issn1424-8220
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/7273
dc.identifier.weblinkhttps://www.mdpi.com/1424-8220/22/2/637
dc.languageen
dc.relation.ispartofSensors
dc.relation.pagesart. 637
dc.rightsCC-BY
dc.sciencecloudnosend
dc.share.typeOPEN_JOURNAL
dc.subject.enchronological age
dc.subject.endental age
dc.subject.enage assessment
dc.subject.endigital pantomography
dc.subject.endigital image analysis
dc.subject.enartificial intelligence
dc.subject.endeep neural network
dc.titleDeep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters
dc.title.volumeSpecial Issue State-of-the-Art Sensors Technology in Poland 2021-2022
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue2
oaire.citation.volume22