Musri, Nabilla and Christie, Brenda and Ichwan, Solachuddin Jauhari Arief and Cahyanto, Arief (2021) Deep learning convolutional neural network algorithms for the early detection and diagnosis of dental caries on periapical radiographs: A systematic review. Imaging Science In Dentistry, 51 (3). pp. 237-242. ISSN 2233-7822, DOI https://doi.org/10.5624/isd.20210074.
Full text not available from this repository.Abstract
Purpose: The aim of this study was to analyse and review deep learning convolutional neural networks for detecting and diagnosing early-stage dental caries on periapical radiographs. Materials and Methods: In order to conduct this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Studies published from 2015 to 2021 under the keywords (deep convolutional neural network) AND (caries), (deep learning caries) AND (convolutional neural network) AND (caries) were systematically reviewed. Results: When dental caries is improperly diagnosed, the lesion may eventually invade the enamel, dentin, and pulp tissue, leading to loss of tooth function. Rapid and precise detection and diagnosis are vital for implementing appropriate prevention and treatment of dental caries. Radiography and intraoral images are considered to play a vital role in detecting dental caries; nevertheless, studies have shown that 20% of suspicious areas are mistakenly diagnosed as dental caries using this technique; hence, diagnosis via radiography alone without an objective assessment is inaccurate. Identifying caries with a deep convolutional neural network-based detector enables the operator to distinguish changes in the location and morphological features of dental caries lesions. Deep learning algorithms have broader and more profound layers and are continually being developed, remarkably enhancing their precision in detecting and segmenting objects. Conclusion: Clinical applications of deep learning convolutional neural networks in the dental field have shown significant accuracy in detecting and diagnosing dental caries, and these models hold promise in supporting dental practitioners to improve patient outcomes.
| Item Type: | Article |
|---|---|
| Funders: | Padjadjaran University [Grant No: 1733/UN6.3.1/LT/2020], Ministry of Education, Malaysia [Grant No: FRGS 15-251-0492] |
| Uncontrolled Keywords: | Deep learning; Neural network models; Dental caries; Radiography; Dental |
| Subjects: | R Medicine > RK Dentistry R Medicine > RK Dentistry > Oral surgery |
| Divisions: | Faculty of Dentistry |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 09 May 2022 02:49 |
| Last Modified: | 09 May 2022 02:49 |
| URI: | http://eprints.um.edu.my/id/eprint/34914 |
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