Three-dimensional digital applications for implant space planning in orthodontics: A narrative review

Three-dimensional measurement and analysis of Mandibular Molar Distalization assisted by micro-implant anchorage combined with clear aligner

Objective

To investigate the effect of micro-implant anchorage combined with a clear aligner on the efficiency of mandibular molar distalization and the protection of anterior teeth anchorage, provide reference for clinical scheme design.

Methods

This is a prospective study. Seventeen patients who were treated in the Orthodontics Department of the Hospital of Stomatology affiliated to Fujian Medical University from 2019 to 2021 and used Invisalign clear aligners to move mandibular molars distally were included and divided into two groups according to anchorage types: Group-A and Group-B. Group-A (ten cases) were treated without micro-implant anchorage, while Group-B (seven cases) were treated with micro-implant anchorage nails for enhanced anchorage. The effect of micro-implant anchorage on crown and root distal movement of mandibular molars and the difference in three-dimensional movement between mandibular molars and mandibular central incisors were analyzed.

Results

The crown distalization efficiency of mandibular first and second molars in Group-B was 68.66% and 71.02%, respectively, which were higher than those in Group-A(p<0.05). The mandibular central incisors in Group-A showed labial displacement and a small amount of elongation, while those in Group-B showed less anchorage loss(p<0.05). In Group-A, the crown was tilted in the distal direction and moved in the buccal direction during mandibular molar distalization(p<0.05). While in Group-B, the crown was tilted in the distal directio (p<0.05) and the mandibular second molar was depressed(p<0.05).

Conclusion

In the process of mandibular molar distalization assisted by micro-implant anchorage combined with a clear aligner, better protects the anchorage of the mandibular central incisor and improves the efficiency of the molar crown distalization.

Impact of 3D imaging techniques and virtual patients on the accuracy of planning and surgical placement of dental implants: A systematic review

Aim

The integration of advanced technologies, including three-dimensional (3D) imaging modalities and virtual simulations, has significantly influenced contemporary approaches to preoperative planning in implant dentistry. Through a meticulous analysis of relevant studies, this review synthesizes findings related to accuracy outcomes in implant placement facilitated by 3D imaging in virtual patients.

Methods

A comprehensive literature search was conducted across relevant databases to identify relevant studies published to date. The inclusion criteria were studies utilizing 3D imaging techniques, virtual patients, and those focusing on the accuracy of dental implant planning and surgical placement. The selected studies were critically appraised for their methodological quality.

Results

After a rigorous analysis, 21 relevant articles were included out of 3021 articles. This study demonstrates the versatility and applicability of these technologies in both in vitro and in vivo settings. Integrating Computer-Aided Design/Computer-Aided Manufacturing (CAD/CAM), cone bean computed tomography (CBCT), and advanced 3D reconstruction methodologies showcases a trend toward enhanced precision in implant planning and placement. Notably, the evaluation parameters varied, encompassing distances, discrepancies, and deviations in the implant placement. The ongoing integration of systems such as dynamic navigation systems, augmented reality, and sophisticated software platforms shows a promising trajectory for the continued refinement of virtual reality applications in dental implantology, providing valuable insights for future research and clinical implementation. Moreover, using stereolithographic surgical guides, virtual planning with CBCT data, and 3D-printed templates consistently demonstrates enhanced precision in dental implant placement compared to traditional methods.

Conclusion

The synthesis of the available evidence underscores the substantial positive impact of 3D imaging techniques and virtual patients on dental implant planning and surgical placement accuracy. Utilizing these technologies contributes to a more personalized and precise approach that enhances overall treatment outcomes. Future research directions and potential refinements to the application of these technologies in clinical practice should be discussed.

Interpretable artificial intelligence for classification of alveolar bone defect in patients with cleft lip and palate

Cleft lip and/or palate (CLP) is the most common congenital craniofacial anomaly and requires bone grafting of the alveolar cleft. This study aimed to develop a novel classification algorithm to assess the severity of alveolar bone defects in patients with CLP using three-dimensional (3D) surface models and to demonstrate through an interpretable artificial intelligence (AI)-based algorithm the decisions provided by the classifier. Cone-beam computed tomography scans of 194 patients with CLP were used to train and test the performance of an automatic classification of the severity of alveolar bone defect. The shape, height, and width of the alveolar bone defect were assessed in automatically segmented maxillary 3D surface models to determine the ground truth classification index of its severity. The novel classifier algorithm renders the 3D surface models from different viewpoints and captures 2D image snapshots fed into a 2D Convolutional Neural Network. An interpretable AI algorithm was developed that uses features from each view and aggregated via Attention Layers to explain the classification. The precision, recall and F-1 score were 0.823, 0.816, and 0.817, respectively, with agreement ranging from 97.4 to 100% on the severity index within 1 group difference. The new classifier and interpretable AI algorithm presented satisfactory accuracy to classify the severity of alveolar bone defect morphology using 3D surface models of patients with CLP and graphically displaying the features that were considered during the deep learning model's classification decision.