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Home Artificial intelligence system for automatic tooth detection and numbering in the mixed...

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

Authors:

  • S. Ozudogru
    Department of Pediatric Dentistry, Faculty of Dentistry, Istanbul Medeniyet University, Istanbul, Turkey
  • E. Gulsen
    Alanya Oral and Dental Health Center, Antalya, Turkey
  • T. Mahyaddinova
    Alanya Oral and Dental Health Center, Antalya, Turkey
  • F. N. Kizilay
    Department of Pediatric Dentistry, Faculty of Dentistry, Inonu University, Malatya, Turkey
  • I. T. Gulsen
    Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Alanya Alaaddin Keykubat University, Antalya, Turkey
  • A. Kuran
    Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Kocaeli University, Kocaeli, Turkey
  • E. Bilgir
    Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey
  • A. F. Aslan
    Department of Mathematics-Computer, Eskisehir Osmangazi University Faculty of Science, Eskisehir, Turkey
  • O. Celik
    Department of Mathematics-Computer, Eskisehir Osmangazi University Faculty of Science, Eskisehir, Turkey
  • I. S. Bayrakdar
    Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey

DOI:

https://doi.org/10.23804/ejpd.2025.2292

ABSTRACT


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.

Background

Identifying and numbering teeth, the initial step in treatment planning, demands an efficient method.

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.

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]).

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Publication date:

June /2025

Issue:

Vol.26 – n.2/2025

Page:

140 – 146

Publisher:

Tecniche Nuove

Topic:

Dental radiographs for infant, children and adolescent

Cite:


Harvard: S. Ozudogru, E. Gulsen, T. Mahyaddinova, F. N. Kizilay, I. T. Gulsen, A. Kuran, E. Bilgir, A. F. Aslan, O. Celik, I. S. Bayrakdar (2025) "Artificial intelligence system for automatic tooth detection and numbering in the mixed dentition in CBCT", European Journal of Paediatric Dentistry, 26(2), pp140-146. doi: 10.23804/ejpd.2025.2292
Vancouver: S. Ozudogru, E. Gulsen, T. Mahyaddinova, F. N. Kizilay, I. T. Gulsen, A. Kuran, E. Bilgir, A. F. Aslan, O. Celik, I. S. Bayrakdar. Artificial intelligence system for automatic tooth detection and numbering in the mixed dentition in CBCT. European Journal of Paediatric Dentistry [Internet]. 2025Jun.26 [cited 2025Jun.14];26(2):140-146. Available from: https://www.ejpd.eu/abstract-pubmed/artificial-intelligence-system-for-automatic-tooth-detection-and-numbering-in-the-mixed-dentition-in-cbct/
MLA: S. Ozudogru, E. Gulsen, T. Mahyaddinova, F. N. Kizilay, I. T. Gulsen, A. Kuran, E. Bilgir, A. F. Aslan, O. Celik, I. S. Bayrakdar Artificial intelligence system for automatic tooth detection and numbering in the mixed dentition in CBCT. European Journal of Paediatric Dentistry. 2025;26(2):140-146

Copyright (c) 2021 Ariesdue

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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    Cristina Calchera
    Editor in chief: dott. Luigi Paglia
    European Journal of Paediatric Dentistry © | ISSN (Online): 2035-648X
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