Lip form responses to changes in maxillary incisor position

Facial profile evaluation and prediction of skeletal class II patients during camouflage extraction treatment: a pilot study

BACKGROUND

The evaluation of the facial profile of skeletal Class II patients with camouflage treatment is of great importance for patients and orthodontists. The aim of this study is to explore the key factors in evaluating the facial profile esthetics and to predict the posttreatment facial profile esthetics of skeletal Class II extraction patients.

METHODS

124 skeletal Class II extraction patients were included. The pretreatment and posttreatment cephalograms were analyzed by a trained expert orthodontist. The facial profile esthetics of pretreatment and posttreatment lateral photographs were evaluated by 10 expert orthodontists using the visual analog scale (VAS). The correlation between subjective facial profile esthetics and objective cephalometric measurements was assessed. Three machine-learning methods were used to predict posttreatment facial profile esthetics.

RESULTS

The distances from lower and upper lip to the E plane and U1-APo showed the stronger correlation with profile esthetics. The changes in lower lip to the E plane and U1-APo during extraction exhibited the stronger correlation with changes in VAS score (r = - 0.551 and r = - 0.469). The random forest prediction model had the lowest mean absolute error and root mean square error, demonstrating a better prediction accuracy and fitting effect. In this model, pretreatment upper lip to E plane, pretreatment Pog-NB and the change of U1-GAll were the most important variables in predicting the posttreatment score of facial profile esthetics.

CONCLUSIONS

The maxillary incisor protrusion and lower lip protrusion are key objective indicators for evaluating and predicting facial profile esthetics of skeletal Class II extraction patients. An artificial intelligence prediction model could be a new method for predicting the posttreatment esthetics of facial profiles.

Application of three-dimensional reconstruction technology in dentistry: a narrative review

BACKGROUND

Three-dimensional(3D) reconstruction technology is a method of transforming real goals into mathematical models consistent with computer logic expressions and has been widely used in dentistry, but the lack of review and summary leads to confusion and misinterpretation of information. The purpose of this review is to provide the first comprehensive link and scientific analysis of 3D reconstruction technology and dentistry to bridge the information bias between these two disciplines.

METHODS

The IEEE Xplore and PubMed databases were used for rigorous searches based on specific inclusion and exclusion criteria, supplemented by Google Academic as a complementary tool to retrieve all literature up to February 2023. We conducted a narrative review focusing on the empirical findings of the application of 3D reconstruction technology to dentistry.

RESULTS

We classify the technologies applied to dentistry according to their principles and summarize the different characteristics of each category, as well as the different application scenarios determined by these characteristics of each technique. In addition, we indicate their development prospects and worthy research directions in the field of dentistry, from individual techniques to the overall discipline of 3D reconstruction technology, respectively.

CONCLUSIONS

Researchers and clinicians should make different decisions on the choice of 3D reconstruction technology based on different objectives. The main trend in the future development of 3D reconstruction technology is the joint application of technology.

Applications of artificial intelligence and machine learning in orthognathic surgery: A scoping review

Over the recent years, Artificial Intelligence (AI) has been progressing rapidly with its ability to mimic human cognitive functions. The potential applications of AI technology in diagnosis, treatment planning, and prognosis prediction have been demonstrated in various studies. The present scoping review aimed to provide an overview of AI and Machine Learning (ML) algorithms and their applications in orthognathic surgery. A comprehensive search was conducted in databases including PubMed, Embase, Scopus, Web of Science and OVID Medline until November 2021. This scoping review was conducted following the PRISMA-ScR guidelines. After applying the inclusion and exclusion criteria, a total of 19 studies were included for final review. AI has profoundly impacted the diagnosis and prediction of orthognathic surgeries with a clinically acceptable accuracy range. Furthermore, AI reduces the work burden of the clinician by eliminating the tedious registration procedures, thereby helping in efficient and automated planning. However, focussing on the research gaps, there is a need to foster the AI models/algorithms to contemporize their efficiency in clinical decision making, diagnosis and surgical planning in future studies.

Development of novel artificial intelligence systems to predict facial morphology after orthognathic surgery and orthodontic treatment in Japanese patients

From a socio-psychological standpoint, improving the morphology of the facial soft-tissues is regarded as an important therapeutic goal in modern orthodontic treatment. Currently, many of the algorithms used in commercially available software programs that are said to provide the function of performing profile prediction are based on the false assumption that the amount of movement of hard-tissue and soft-tissue has a proportional relationship. The specification of the proportionality constant value depends on the operator, and there is little evidence to support the validity of the prediction result. Thus, the present study attempted to develop artificial intelligence (AI) systems that predict the three-dimensional (3-D) facial morphology after orthognathic surgery and orthodontic treatment based on the results of previous treatment. This was a retrospective study in a secondary adult care setting. A total of 137 patients who underwent orthognathic surgery (n = 72) and orthodontic treatment with four premolar extraction (n = 65) were enrolled. Lateral cephalograms and 3-D facial images were obtained before and after treatment. We have developed two AI systems to predict facial morphology after orthognathic surgery (System S) and orthodontic treatment (System E) using landmark-based geometric morphometric methods together with deep learning methods; where cephalometric changes during treatment and the coordinate values of the faces before treatment were employed as predictive variables. Eleven-fold cross-validation showed that the average system errors were 0.94 mm and 0.69 mm for systems S and E, respectively. The total success rates, when success was defined by a system error of < 1 mm, were 54% and 98% for systems S and E, respectively. The total success rates when success was defined by a system error of < 2 mm were both 100%. AI systems to predict facial morphology after treatment were therefore confirmed to be clinically acceptable.

The effects of computer-aided anteroposterior maxillary incisor movement on ratings of facial attractiveness

BACKGROUND

Before planning orthodontic treatment, it is necessary to understand societal preferences for facial esthetics. The anteroposterior (AP) position of the maxillary incisors affects the appearance of the soft tissue profile and can be manipulated by orthodontic techniques. To improve the ability to predict the most suitable maxillary incisor position, numerous cephalometric and profilometric measurements have been suggested. Among them are the Six Elements to Orofacial Harmony proposed by L. F. Andrews, whereby forehead angulation is used to dictate maxillary incisor sagittal position. Our aim was to evaluate differences in preference for the AP position of the maxillary incisor between orthodontic and lay panels.

METHODS

A smiling profile photograph was taken of a female subject who best fit the chosen soft tissue normative values and whose maxillary incisors were in an Element II position. The photograph was manipulated to simulate maxillary protrusion and retrusion at 1-mm increments to a maximum of +/-4 mm. Panels of orthodontists and nonorthodontists scored the attractiveness of the photographic variations according to a 100-mm visual analogue scale.

RESULTS

The 4-mm retrusive photograph was significantly less desirable than all others, which suggests that, from an esthetic standpoint, it is preferable to either leave a normally protrusive maxillary dentition where it is or advance rather than retract the maxillary anterior teeth. Orthodontic training did not significantly affect the magnitude of the ratings or pattern of preference in our sample.

CONCLUSIONS

Andrews' Element II provides an additional useful method to evaluate attractiveness relative to the maxillary incisor position.