Correlation- and covariance-supported normalization method for estimating orthodontic trainer treatment for clenching activity

Applications of artificial intelligence and machine learning in orthodontics: a scoping review

Introduction

This scoping review aims to provide an overview of the existing evidence on the use of artificial intelligence (AI) and machine learning (ML) in orthodontics, its translation into clinical practice, and what limitations do exist that have precluded their envisioned application.

Methods

A scoping review of the literature was carried out following the PRISMA-ScR guidelines. PubMed was searched until July 2020.

Results

Sixty-two articles fulfilled the inclusion criteria. A total of 43 out of the 62 studies (69.35%) 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 non-orthodontic journals (36) was more extensive than in orthodontic journals (26). 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 or specific (33), automated anatomic landmark detection and/or analyses (19), assessment of growth and development (4), and evaluation of treatment outcomes (2). The different characteristics and distribution of these studies have been displayed and elucidated upon therein.

Conclusion

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40510-021-00361-9.

Features for muscle fatigue computed from electromyogram and mechanomyogram: A new one

Muscle fatigue produces negative effects in the performance and it may lead to a muscle failure. This problem makes the quantitative grading of muscle fatigue a necessity in ergonomic and physiological research. Moreover, the quantitative grading of muscle fatigue is needed to increase work and sport productivity and prevent several accidents that result from muscle fatigue. Even though there are many studies for this aim, there is no quantitative criterion for the evaluation of muscle fatigue. The main reason is that muscle fatigue is a complex physiological situation that is dependent on several parameters. Our aim in this study is to present a new feature to evaluate muscle fatigue and prove the reliability of the new feature by making correlation analyses between this with other features. For this aim, electromyography and mechanomyography signals were simultaneously recorded from the biceps brachii and triceps brachii muscles during the isometric and isotonic contractions of 60 healthy volunteers (30 females, 30 males). The mean power frequency and median frequency, which are used in the literature, were compared to the frequency ratio change, the new measure; correlations between the frequency ratio change and the mean power frequency and median frequency were analysed. There was a high correlation between the features, and frequency ratio change can be used to quantitatively evaluate muscle fatigue.