Hartman, Henri and Nurdin, Denny and Akbar, Saiful and Cahyanto, Arief and Setiawan, Arlette Suzy (2024) Exploring the potential of artificial intelligence in paediatric dentistry: A systematic review on deep learning algorithms for dental anomaly detection. International Journal of Paediatric Dentistry, 34 (5). pp. 639-652. ISSN 0960-7439, DOI https://doi.org/10.1111/ipd.13164.
Full text not available from this repository.Abstract
BackgroundArtificial intelligence (AI) based on deep learning (DL) algorithms has shown promise in enhancing the speed and accuracy of dental anomaly detection in paediatric dentistry.AimThis systematic review aimed to investigate the performance of AI systems in identifying dental anomalies in paediatric dentistry and compare it with human performance.DesignA systematic search of Scopus, PubMed and Google Scholar was conducted from 2012 to 2022. Inclusion criteria were based on problem/patient/population, intervention/indicator, comparison and outcome scheme and specific keywords related to AI, DL, paediatric dentistry, dental anomalies, supernumerary and mesiodens. Six of 3918 initial pool articles were included, assessing nine DL sub-systems that used panoramic radiographs or cone-beam computed tomography. Article quality was assessed using QUADAS-2.ResultsArtificial intelligence systems based on DL algorithms showed promising potential in enhancing the speed and accuracy of dental anomaly detection, with an average of 85.38% accuracy and 86.61% sensitivity. Human performance, however, outperformed AI systems, achieving 95% accuracy and 99% sensitivity. Limitations included a limited number of articles and data heterogeneity.ConclusionThe potential of AI systems employing DL algorithms is highlighted in detecting dental anomalies in paediatric dentistry. Further research is needed to address limitations, explore additional anomalies and establish the broader applicability of AI in paediatric dentistry.
| Item Type: | Article |
|---|---|
| Funders: | Faculty of Dentistry at Universitas Jenderal Achmad Yani, Universitas Padjadjaran, Universiti Malaya |
| Uncontrolled Keywords: | artificial intelligence; cone-beam computed tomography; deep learning; dental anomalies; panoramic radiography |
| Subjects: | R Medicine > RK Dentistry |
| Divisions: | Faculty of Dentistry > Department of Restorative Dentistry |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 06 Jan 2025 02:09 |
| Last Modified: | 06 Jan 2025 02:09 |
| URI: | http://eprints.um.edu.my/id/eprint/47056 |
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