Proceedings of the 4th International Conference on Informatics, Technology and Engineering 2023 (InCITE 2023)

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Alveolar Bone Quality Classification from Dental Cone Beam Computed Tomography Images using YOLOv4-tiny

Authors
Monica Widiasri1, 2, *, Nanik Suciati1, Chastine Fatichah1, Eha Renwi Astuti3, Ramadhan Hardani Putra3, Agus Zainal Arifin1
1Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
2Department of Informatics, Universitas Surabaya, Surabaya, Indonesia
3Department of Dentomaxillofacial Radiology, Universitas Airlangga, Surabaya, Indonesia
Corresponding Author
Monica Widiasri
Available Online 19 November 2023.
DOI
10.2991/978-94-6463-288-0_48How to use a DOI?
Keywords
Alveolar Bone; Bone Quality; Classification; CBCT images; Dental Implant; Detection; YOLO
Abstract

Bone quality is essential in dental implant planning for successful implant placement. Bone quality can be determined based on bone density observed from Beam Computed Tomography (CBCT) images which are commonly used in dental implant planning. The most accepted classification of alveolar bone quality is that proposed by Lekholm and Zarb (1985), classifying bone into four types based on the density of cortical and trabecular bone observed from CBCT images. Currently, determining the type of alveolar bone in the implant area depends on the clinician’s subjectivity. This study uses deep learning to propose an alveolar bone quality classification system from CBCT images. The YOLOv4-tiny method, a detection and classification method with excellent performance and fast training time, was used to detect and classify alveolar bone from 2D dental CBCT images of mandibular coronal slices. The results of bone quality classification yielded a mean precision value of 99.91%. The study findings indicate that YOLOv4-tiny can accurately classify alveolar bone density. This precision is essential for proper dental implant placement and implant planning.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 4th International Conference on Informatics, Technology and Engineering 2023 (InCITE 2023)
Series
Atlantis Highlights in Engineering
Publication Date
19 November 2023
ISBN
978-94-6463-288-0
ISSN
2589-4943
DOI
10.2991/978-94-6463-288-0_48How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Monica Widiasri
AU  - Nanik Suciati
AU  - Chastine Fatichah
AU  - Eha Renwi Astuti
AU  - Ramadhan Hardani Putra
AU  - Agus Zainal Arifin
PY  - 2023
DA  - 2023/11/19
TI  - Alveolar Bone Quality Classification from Dental Cone Beam Computed Tomography Images using YOLOv4-tiny
BT  - Proceedings of the 4th International Conference on Informatics, Technology and Engineering 2023 (InCITE 2023)
PB  - Atlantis Press
SP  - 584
EP  - 593
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-288-0_48
DO  - 10.2991/978-94-6463-288-0_48
ID  - Widiasri2023
ER  -