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- Title
Deep learning in periodontology and oral implantology: A scoping review.
- Authors
Mohammad‐Rahimi, Hossein; Motamedian, Saeed Reza; Pirayesh, Zeynab; Haiat, Anahita; Zahedrozegar, Samira; Mahmoudinia, Erfan; Rohban, Mohammad Hossein; Krois, Joachim; Lee, Jae‐Hong; Schwendicke, Falk
- Abstract
Deep learning (DL) has been employed for a wide range of tasks in dentistry. We aimed to systematically review studies employing DL for periodontal and implantological purposes. A systematic electronic search was conducted on four databases (Medline via PubMed, Google Scholar, Scopus, and Embase) and a repository (ArXiv) for publications after 2010, without any limitation on language. In the present review, we included studies that reported deep learning models' performance on periodontal or oral implantological tasks. Given the heterogeneities in the included studies, no meta‐analysis was performed. The risk of bias was assessed using the QUADAS‐2 tool. We included 47 studies: focusing on imaging data (n = 20) and non‐imaging data in periodontology (n = 12), or dental implantology (n = 15). The detection of periodontitis and gingivitis or periodontal bone loss, the classification of dental implant systems, or the prediction of treatment outcomes in periodontology and implantology were major use cases. The performance of the models was generally high. However, it varied given the employed methods (which includes various types of convolutional neural networks (CNN) and multi‐layered perceptron (MLP)), the variety in specific modeling tasks, as well as the chosen and reported outcomes, outcome measures and outcome level. Only a few studies (n = 7) showed a low risk of bias across all assessed domains. A growing number of studies evaluated DL for periodontal or implantological objectives. Heterogeneity in study design, poor reporting and a high risk of bias severely limit the comparability of studies and the robustness of the overall evidence.
- Subjects
DEEP learning; DENTAL implants; ONLINE information services; MEDICAL information storage &; retrieval systems; SYSTEMATIC reviews; MACHINE learning; DESCRIPTIVE statistics; LITERATURE reviews; MEDLINE; ARTIFICIAL neural networks; PERIODONTICS
- Publication
Journal of Periodontal Research, 2022, Vol 57, Issue 5, p942
- ISSN
0022-3484
- Publication type
Article
- DOI
10.1111/jre.13037