Convolutional neural networks for automated tooth numbering on panoramic radiographs: A scoping review.

Autor:	Putra RH; Department of Dentomaxillofacial Radiology, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia., Astuti ER; Department of Dentomaxillofacial Radiology, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia., Nurrachman AS; Department of Dentomaxillofacial Radiology, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia., Putri DK; Department of Dentomaxillofacial Radiology, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia.; Division of Dental Informatics and Radiology, Tohoku University Graduate School of Dentistry, Sendai, Japan., Ghazali AB; Oral Radiology Unit, Department of Oral Maxillofacial Surgery and Oral Diagnosis, Kulliyyah of Dentistry, International Islamic University Malaysia, Malaysia., Pradini TA; Undergraduate Program, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia., Prabaningtyas DT; Undergraduate Program, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia.
Jazyk:	angličtina
Zdroj:	Imaging science in dentistry [Imaging Sci Dent] 2023 Dec; Vol. 53 (4), pp. 271-281. Date of Electronic Publication: 2023 Sep 04.
DOI:	10.5624/isd.20230058
Abstrakt:	Purpose: The objective of this scoping review was to investigate the applicability and performance of various convolutional neural network (CNN) models in tooth numbering on panoramic radiographs, achieved through classification, detection, and segmentation tasks. Material and Methods: An online search was performed of the PubMed, Science Direct, and Scopus databases. Based on the selection process, 12 studies were included in this review. Results: Eleven studies utilized a CNN model for detection tasks, 5 for classification tasks, and 3 for segmentation tasks in the context of tooth numbering on panoramic radiographs. Most of these studies revealed high performance of various CNN models in automating tooth numbering. However, several studies also highlighted limitations of CNNs, such as the presence of false positives and false negatives in identifying decayed teeth, teeth with crown prosthetics, teeth adjacent to edentulous areas, dental implants, root remnants, wisdom teeth, and root canal-treated teeth. These limitations can be overcome by ensuring both the quality and quantity of datasets, as well as optimizing the CNN architecture. Conclusion: CNNs have demonstrated high performance in automated tooth numbering on panoramic radiographs. Future development of CNN-based models for this purpose should also consider different stages of dentition, such as the primary and mixed dentition stages, as well as the presence of various tooth conditions. Ultimately, an optimized CNN architecture can serve as the foundation for an automated tooth numbering system and for further artificial intelligence research on panoramic radiographs for a variety of purposes. Competing Interests: Conflicts of Interest: None (Copyright © 2023 by Korean Academy of Oral and Maxillofacial Radiology.)
Databáze:	MEDLINE
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