Artificial Intelligence in Orthodontics: A Review Article


Authors : Sharath Kumar Shetty; Vijayananda Kumara M; Visakh G Panicker

Volume/Issue : Volume 7 - 2022, Issue 11 - November

Google Scholar : https://bit.ly/3IIfn9N

Scribd : https://bit.ly/3Wm52qT

DOI : https://doi.org/10.5281/zenodo.7547674

This review aims to determine the applications of ArtificialIntelligence (AI) that are extensively employed in the field of Orthodontics, to evaluate its benefits, and to discuss its potential implications in this speciality. Recentdecades have witnessed enormous changes in our profession. The arrival of newand more aesthetic options in orthodontic treatment, the transition to a fully digitalworkflow, the emergence of temporary anchorage devices and new imaging methods all provide both patients and professionals with a new focus in orthodontic care.A scoping review of the literature was carried out following the PRISMA-ScR guidelines. PubMed was searched until July 2022.Additionalmanual searches were performed.Forty four articles fulfilled the inclusion criteria. A total of 30 out of the 44 studies (68.18%) were published this last decade. The majority of these studies were from the USA (11), followed by South Korea (9) and China (7). The number of studies published in nonorthodontic journals (26) was more extensive than in orthodontic journals (18). Artificial Neural Networks (ANNs) were found to be the most commonly utilized AI/ML algorithm (13 studies), followed by Convolutional Neural Networks (CNNs), Support Vector Machine (SVM) (9 studies each), and regression (8 studies). The most commonly studied domains were diagnosis and treatment planning—either broad-based orspecific (33), assessment of growth and development(4), and evaluation of treatment outcomes (2). The different characteristics and distribution of these studies havebeen displayed and elucidated upon therein. This scoping review suggests that there has been an exponential increase in the number of studies involving various orthodontic applications of AI and ML. The most commonly studied domains were diagnosis and treatment planning, automated anatomic landmark detection and/or analyses, and growth and development assessment. In the growth and development research area, the Cervical Vertebral Maturation stage can be determined using an Artificial Neural Network model and obtain the same results as expert human observers. AI technology can also improve the diagnostic accuracy for orthodontic treatments.

Keywords : artificial intelligence, machine learning, orthodontics, review

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