Abstract
This scoping review aims to determine the applications of Artificial Intelligence (AI) that are extensively employed in the field of Orthodontics, to evaluate its benefits, and to discuss its potential implications in this speciality. Recent decades have witnessed enormous changes in our profession. The arrival of new and more aesthetic options in orthodontic treatment, the transition to a fully digital workflow, the emergence of temporary anchorage devices and new imaging methods all provide both patients and professionals with a new focus in orthodontic care. This review was performed following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. The electronic literature search was performed through MEDLINE/PubMed, Scopus, Web of Science, Cochrane and IEEE Xplore databases with a 11-year time restriction: January 2010 till March 2021. No additional manual searches were performed. The electronic literature search initially returned 311 records, and 115 after removing duplicate references. Finally, the application of the inclusion criteria resulted in 17 eligible publications in the qualitative synthesis review. The analysed studies demonstrated that Convolution Neural Networks can be used for the automatic detection of anatomical reference points on radiological images. 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, thereby helping the orthodontist work more accurately and efficiently.
Highlights
The type of Machine learning (ML) method, the number and type of images used for testing Artificial Intelligence (AI) software, the accuracy of the technique, and its benefits to the field of orthodontics were extracted from the articles
The use of automated cephalometric points identification or automated teeth segmentation to enable a treatment preview outcome helps reduce orthodontic treatment planning times.5,10-13 with deep learning techniques it is possible to eliminate the subjectivity associated with human decision-making; traditional manual methods are likely to incorporate a relatively higher degree of intra- and inter- observer errors due to that subjectivity, which can lead to an increase in the prediction error.[32]
This review presents two main limitations: First, being a scoping review, the review question has to be more generally defined when compared to a systematic review
Summary
This review was performed following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines.[8] A pilot search of MEDLINE What is the applicability of Artificial Intelligence in the field of Orthodontics?. Patients’ diagnostic images (orthopantomography, cephalometric radiographs, intraoral radiographs, CBCT† , clinical images, facial images and 3D model images). Artificial intelligence-based forms of diagnosis and treatment planning. Measurable or predictive outcomes such as accuracy, sensitivity and specificity. PubMed) was conducted to prepare the study protocol. The data extraction forms were constructed after the initial results of the pilot search. The search was based on the PICO (problem/patient/population, intervention/indicator, comparison and outcome) elements (Table 1)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
