In recent years, there has been the notable emergency of artificial intelligence (AI) as a transformative force in multiple domains, including orthodontics. This review aims to provide a comprehensive overview of the present state of AI applications in orthodontics, which can be categorized into the following domains: (1) diagnosis, including cephalometric analysis, dental analysis, facial analysis, skeletal-maturation-stage determination and upper-airway obstruction assessment; (2) treatment planning, including decision making for extractions and orthognathic surgery, and treatment outcome prediction; and (3) clinical practice, including practice guidance, remote care, and clinical documentation. We have witnessed a broadening of the application of AI in orthodontics, accompanied by advancements in its performance. Additionally, this review outlines the existing limitations within the field and offers future perspectives.

Introduction: The electronic dental model (e‐model) is an example of a digital 3‐dimensional technology to support inquiry‐based learning in undergraduate dental education. As student perceptions of and engagement with e‐models vary, it is uncertain whether these perceptions have implications for their learning processes and outcomes.Materials and Methods: Third‐year dental students (N = 40) completed a questionnaire to identify their perceptions of and preferences for model modalities. They were divided into three groups based on their preference: Preferring plaster models (Group 1); Preferring e‐models (Group 2); No preference (Group 3). Students from three groups (N = 9) attended a hands‐on digital occlusion evaluation workshop, and then completed a case‐based diagnostic evaluation test using digital occlusion evaluation software. Camtasia Studio™ recorded real‐time and on‐screen data of the number of mouse‐clicks and time spent.Results: Students reported positive feedbacks on the use of e‐models, and 72.5% of the students preferred combination use of e‐models and plaster models. After attending the hands‐on digital dental occlusion evaluation workshop, Group 2 scored higher on the diagnostic evaluation test (p < .05) and registered more mouse‐clicks than Group 1 when evaluating the arch symmetry (p < .05). Group 2 registered fewer mouse‐clicks than Group 3 during tooth size measurement (p < .05). There was no significant difference regarding the time used to answer the knowledge questions amongst the three groups.Conclusion: Undergraduate dental students indicated a generally high acceptance of e‐models for their learning in orthodontics, and more prefer a blended approach. Students preferring e‐models presented higher performance outcomes, which supports cognitive load theory regarding prior exposure to simulation‐based environments.

Radiomics analysis can potentially characterize salivary gland tumors (SGTs) on magnetic resonance imaging (MRI). The procedures for radiomics analysis were various, and no consistent performances were reported. This review evaluated the methodologies and performances of studies using radiomics analysis to characterize SGTs on MRI. We systematically reviewed studies published until July 2023, which employed radiomics analysis to characterize SGTs on MRI. In total, 14 of 98 studies were eligible. Each study examined 23–334 benign and 8–56 malignant SGTs. Least absolute shrinkage and selection operator (LASSO) was the most common feature selection method (in eight studies). Eleven studies confirmed the stability of selected features using cross-validation or bootstrap. Nine classifiers were used to build models that achieved area under the curves (AUCs) of 0.74 to 1.00 for characterizing benign and malignant SGTs and 0.80 to 0.96 for characterizing pleomorphic adenomas and Warthin’s tumors. Performances were validated using cross-validation, internal, and external datasets in four, six, and two studies, respectively. No single feature consistently appeared in the final models across the studies. No standardized procedure was used for radiomics analysis in characterizing SGTs on MRIs, and various models were proposed. The need for a standard procedure for radiomics analysis is emphasized.

Automated tooth segmentation and identification on dental radiographs are crucial steps in establishing digital dental workflows. While deep learning networks have been developed for these tasks, their performance has been inferior in partially edentulous individuals. This study proposes a novel semi-supervised Transformer-based framework (SemiTNet), specifically designed to improve tooth segmentation and identification performance on panoramic radiographs, particularly in partially edentulous cases, and establish an open-source dataset to serve as a unified benchmark. A total of 16,317 panoramic radiographs (1589 labeled and 14,728 unlabeled images) were collected from various datasets to create a large-scale dataset (TSI15k). The labeled images were divided into training and test sets at a 7:1 ratio, while the unlabeled images were used for semi-supervised learning. The SemiTNet was developed using a semi-supervised learning method with a label-guided teacher–student knowledge distillation strategy, incorporating a Transformer-based architecture. The performance of SemiTNet was evaluated on the test set using the intersection over union (IoU), Dice coefficient, precision, recall, and F1 score, and compared with five state-of-the-art networks. Paired t-tests were performed to compare the evaluation metrics between SemiTNet and the other networks. SemiTNet outperformed other networks, achieving the highest accuracy for tooth segmentation and identification, while requiring minimal model size. SemiTNet’s performance was near-perfect for fully dentate individuals (all metrics over 99.69%) and excellent for partially edentulous individuals (all metrics over 93%). In edentulous cases, SemiTNet obtained statistically significantly higher tooth identification performance than all other networks. The proposed SemiTNet outperformed previous high-complexity, state-of-the-art networks, particularly in partially edentulous cases. The established open-source TSI15k dataset could serve as a unified benchmark for future studies.

Skeletal Class III malocclusion is one type of dentofacial deformity that significantly affects patients’ facial aesthetics and oral health. The orthodontic treatment of skeletal Class III malocclusion presents challenges due to uncertainties surrounding mandibular growth patterns and treatment outcomes. In recent years, disease-specific radiographic features have garnered interest from researchers in various fields including orthodontics, for their exceptional performance in enhancing diagnostic precision and treatment effect predictability. The aim of this narrative review is to provide an overview of the valuable radiographic features in the diagnosis and management of skeletal Class III malocclusion. Based on the existing literature, a series of analyses on lateral cephalograms have been concluded to identify the significant variables related to facial type classification, growth prediction, and decision-making for tooth extractions and orthognathic surgery in patients with skeletal Class III malocclusion. Furthermore, we summarize the parameters regarding the inter-maxillary relationship, as well as different anatomical structures including the maxilla, mandible, craniofacial base, and soft tissues from conventional and machine learning statistical models. Several distinct radiographic features for Class III malocclusion have also been preliminarily observed using cone beam computed tomography (CBCT) and magnetic resonance imaging (MRI).

Maxillary deficiency, a common transversal dentofacial deformity, affects aesthetics and function. Timely intervention during adolescence is crucial, as the growth potential of the maxilla provides an opportunity to optimize treatment outcomes. This review explores various approaches for adolescent maxillary expansion, including orthopedic and surgical methods. Orthopedic appliances effectively address transverse deficiencies without surgery and are particularly beneficial in managing conditions in children and early adolescents. In mid- to late-stage adolescents, bone-borne devices with mini-surgery offer better skeletal expansion outcomes. However, in cases of severe deficiencies, or where skeletal resistance limits non-surgical methods, surgical interventions become essential. Procedures like surgically assisted maxillary expansion and orthognathic surgery offer superior skeletal corrections. These techniques are particularly valuable for late adolescents with complex conditions. This review comprehensively summarizes the applications, outcomes, and limitations of these treatment options, highlighting the importance of selecting individualized, growth stage-appropriate interventions.