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

Putra, Ramadhan Hardani and Astuti, Eha Renwi and Nurrachman, Aga Satria and Putri, Dina Karimah and Ghazali, Ahmad Badruddin and Pradini, Tjio Andrinanti and Prabaningtyas, Dhinda Tiara (2023) Convolutional neural networks for automated tooth numbering on panoramic radiographs: a scoping review. Imaging Science in Dentistry, 53 (4). pp. 271-281. ISSN 2233-7822 E-ISSN 2233-7830

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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. Materials 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.

Item Type: Article (Journal)
Uncontrolled Keywords: Artificial Intelligence; Technology Transfer; Deep Learning; Dentition; Radiography, Panoramic
Subjects: R Medicine > RK Dentistry
R Medicine > RK Dentistry > RK318 Oral and Dental Medicine. Pathology. Diseases-Therapeutics-General Works
R Medicine > RK Dentistry > RK529 Oral Surgery-General Works
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Dentistry
Kulliyyah of Dentistry > Department of Oral Maxillofacial Surgery and Oral Diagnosis
Depositing User: Dr Ahmad Badruddin Ghazali
Date Deposited: 25 Sep 2023 08:55
Last Modified: 20 May 2024 15:17
URI: http://irep.iium.edu.my/id/eprint/107041

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