Deep learning in periodontology and oral implantology: A scoping review

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.

Automated deep learning for classification of dental implant radiographs using a large multi-center dataset

“…AI-based large-scale machine learning and DL in the late 2010s, which facilitated the accurate diagnosis of medical radiographic images, garnered attention in biomedical engineering and provided novel insights into precision medicine 26 – 28 . More recently, deep convolutional neural network algorithms have gained popularity in dentistry, and have also achieved considerable success in analyzing dental radiographic images 29 . The potential clinical applications of DL technology are closely related to (1) deeper and more sophisticated neural network structures and (2) large annotated and high-quality datasets.…”

The application of artificial neural networks in the detection of mandibular fractures using panoramic radiography

“…The use of these intelligent technologies has been used as a powerful tool for the prediction and diagnosis of diseases as well as for the provision of appropriate treatment plans by dentists. [ 1 2 ] It is possible for dentists to use AI technology to make more accurate diagnoses and better clinical decisions. AI is the ability of a system to imitate human-like intelligence.…”

Deep learning and clustering approaches for dental implant size classification based of periapical radiographs

“…The application of artificial intelligence (AI) in medical imaging analysis has expanded rapidly [9][10][11][12][13][14] . Also, in dentistry, AI models were used for the diagnosis of dental diseases such as dental caries or periodontal disease [15][16][17] .…”