Artificial intelligence system for automatic tooth detection and numbering in the mixed dentition in CBCT

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dc.authorid0000-0001-5415-6084
dc.authorid0000-0002-1014-4417
dc.contributor.authorOzudogru, S.
dc.contributor.authorGulsen, E.
dc.contributor.authorMahyaddinova, T.
dc.contributor.authorKizilay, F. N.
dc.contributor.authorGulsen, I. T.
dc.contributor.authorKuran, A.
dc.contributor.authorBilgir, E.
dc.date.accessioned2026-01-24T12:29:22Z
dc.date.available2026-01-24T12:29:22Z
dc.date.issued2025
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractBackground Identifying and numbering teeth, the initial step in treatment planning, demands an efficient method. Aim To evaluate the effectiveness and accuracy of artificial intelligence (AI) by automating tooth segmentation in CBCT volumes of paediatric patients with mixed dentition, using nnU-Netv2 algorithm. Study Design In 49 CBCT scans, automatic segmentation and numbering of erupted/unerupted teeth of mixed dentition patients were performed using the CranioCatch labelling software (Eskisehir, Turkey). The dataset was randomly split into training (90%) and test (10%) groups. The developed model was trained with 1000 epochs using CBCT volumes and labelling. The performance of the model in numbering deciduous and permanent teeth was evaluated using several parameters, Dice Coefficient (DC), Jaccard index (Intersection over Union [IoU]). Results The accuracy, precision, and recall values for the successful numbering of deciduous and permanent teeth in CBCT scans were determined to be 0.99, 0.86, and 0.84, respectively. The values for the DC, Jaccard index, and 95% HD were calculated as 0.81, 0.81 and 1.93, respectively. Conclusion AI models offer a promising approach in the mixed dentition period and play a valuable role in dentists' planning in terms of time and effort.
dc.identifier.doi10.23804/ejpd.2025.2292
dc.identifier.endpage146
dc.identifier.issn1591-996X
dc.identifier.issn2035-648X
dc.identifier.issue2
dc.identifier.pmid40008830
dc.identifier.scopus2-s2.0-105008084205
dc.identifier.scopusqualityQ1
dc.identifier.startpage140
dc.identifier.urihttps://doi.org/10.23804/ejpd.2025.2292
dc.identifier.urihttps://hdl.handle.net/20.500.12868/5317
dc.identifier.volume26
dc.identifier.wosWOS:001529030000013
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherAriesdue Srl
dc.relation.ispartofEuropean Journal of Paediatric Dentistry
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260121
dc.subjectArtificial intelligence
dc.subjecttooth detecting
dc.subjectCBCT
dc.subjectpaediatric dentistry
dc.subjectmixed dentition
dc.titleArtificial intelligence system for automatic tooth detection and numbering in the mixed dentition in CBCT
dc.typeArticle

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