Deep learning in periodontology and oral implantology: A scoping review

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) an...

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Vydané v:Journal of periodontal research Ročník 57; číslo 5; s. 942 - 951
Hlavní autori: Mohammad‐Rahimi, Hossein, Motamedian, Saeed Reza, Pirayesh, Zeynab, Haiat, Anahita, Zahedrozegar, Samira, Mahmoudinia, Erfan, Rohban, Mohammad Hossein, Krois, Joachim, Lee, Jae‐Hong, Schwendicke, Falk
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Wiley Subscription Services, Inc 01.10.2022
Predmet:
Alveolar Bone Loss
artificial intelligence
Bias
Bone loss
Deep Learning
Dental implants
Dentistry
Gingivitis
Humans
machine learning
Meta-analysis
Neural networks
Periodontics
Periodontitis
review
machine learning
periodontics
review
artificial intelligence
dental implants
neural networks
ISSN:0022-3484, 1600-0765, 1600-0765
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Popis
Shrnutí: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.
Bibliografia:Hossein Mohammad‐Rahimi and Saeed Reza Motamedian are co‐first authors.
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ISSN:0022-3484
1600-0765
1600-0765
DOI:10.1111/jre.13037