Class II malocclusions, characterized by the mesial positioning of the maxillary molars relative to the mandibular molars, are among the most frequently encountered orthodontic issues. One of the widely adopted non-extraction approaches for addressing this malocclusion is maxillary molar distalization, which has been utilized for decades to create space within the dental arch. Historically, extraoral appliances such as headgear were commonly employed. However, with technological advancements, intraoral distalization devices, particularly those incorporating temporary anchorage devices (TADs), have gained prominence due to their compact size, not being visible externally, and improved patient acceptance. These appliances offer significant advantages, including being less invasive compared with extraction-based camouflage treatments, being more readily accepted by patients due to the absence of extraction spaces, and not requiring the complex biomechanical principles involved in extraction-based methods. TADs have revolutionized distalization techniques by providing superior anchorage control, reducing treatment duration, and offering a straightforward, patient-friendly application. The purpose of this comprehensive narrative review is to focus specifically on intraoral distalization techniques utilizing TADs, summarizing their efficacy and outcomes in the management of Class II malocclusions as reported in clinical studies over the past three decades.

This study aimed to evaluate the effect of resin type and printing design on the dimensional accuracy of three dimensional (3D) printed orthodontic models, considering their clinical relevance for applications such as in-house aligner fabrication. Since low-cost Liquid Crystal Display (LCD) printers have been increasingly adopted in practice but data on their trueness and precision with different resins and print designs were limited, the study sought to provide evidence-based insights into their reliability. A mandibular model was designed using Blenderfordental (B4D, version 1.1.2024; Dubai, United Arab Emirates) software and fabricated with the Anycubic Photon Mono 7 Pro 14K (Anycubic, Shenzhen, China) LCD printer. The model was printed in vertical orientation using three different print designs at two layer thicknesses (50 µm and 100 µm). Four resins (Elegoo, Anycubic, eSUN, and Phrozen) were used, and each resin was printed with all three designs, yielding 126 models per resin and a total of 504 printed models. Dimensional deviations between the printed and reference models were assessed using root mean square (RMS) values and color-coded deviation maps. Significant differences in trueness were found among resins and print designs at both layer thicknesses (p < 0.001). At a layer thickness of 50 µm, eSUN and Anycubic showed superior trueness, whereas Phrozen exhibited the highest deviations. At a layer thickness of 100 µm, Anycubic, eSUN, and Phrozen generally performed better than Elegoo. Overall, printing at 100 µm yielded better performance than at 50 µm. Precision analysis revealed resin-dependent differences, with eSUN showing significantly higher precision than Elegoo at both layer thicknesses (p = 0.006 at 100 µm, p < 0.001 at 50 µm) and superior precision compared to Phrozen at 50 µm (p = 0.019). Both resin selection and print design significantly affect the dimensional accuracy of 3D-printed dental models.

Background: Artificial intelligence (AI) is increasingly integrated into orthodontic workflows, including digital model analysis modules embedded in orthodontic software. While these systems offer efficiency and automation, the accuracy and clinical reliability of AI-generated measurements and diagnostic assessments remain unclear. Therefore, to use AI systems safely and effectively in clinical orthodontics, it is important to check their results by comparing them with those of experienced orthodontists. Methods: Digital models of 48 patients were analyzed by the Orthodontist group and two AI platforms: Titan (full) and SoftSmile (Bolton only). Three orthodontists independently measured all variables using 3Shape OrthoAnalyzer, and group means were used for comparison. A subset of models was reanalyzed after two weeks to assess consistency. Data distribution was evaluated, and appropriate statistical tests were applied. Reliability was assessed using intraclass correlation coefficients (ICC) and Cohen’s kappa. Results: Almost perfect agreement was observed between the orthodontists and Titan AI in molar classification (κ = 0.955 right, κ = 0.900 left; p < 0.001), with perfect agreement reported across all groups—including between the orthodontists themselves—for Angle classification (κ = 1.00). In anterior and overall Bolton analyses, no meaningful agreement was found between the orthodontists and AI platforms. However, in a subset of patients where all three methods identified the tooth size discrepancy in the same arch (either maxilla or mandible), no significant differences were found in anterior (p = 0.226) or overall Bolton values (p = 0.795). Overjet, overbite, and space analysis values showed significant differences between the orthodontist and Titan groups (p < 0.001). ICC analysis indicated good to excellent intra- and inter-rater reliability within the orthodontist group (≥0.77), while both AI systems demonstrated excellent internal consistency, with ICC values exceeding 0.95. Conclusions: AI-based platforms showed high agreement with orthodontists only in Angle classification. While their performance in Bolton analysis was limited, significant differences were observed in other linear measurements, indicating the need for further refinement before clinical use.

This study aimed to evaluate total maxillary arch distalization using three different skeletal anchorage systems—Mini Screw-Assisted Keleş Slider (MKS), infrazygomatic crest (IZC) screw, and maxillary tuberosity (MT) screw—through finite element analysis (FEA). A 3D cranio-maxillary model including dentition, periodontal ligament, and alveolar bone was constructed. For the MKS and IZC groups, forces were applied at three vertical heights (0 mm, 3 mm, and 6 mm apically), while the MT group involved three directional applications: buccal, palatal, and combined bucco-palatal. This design yielded nine distinct simulation scenarios. Tooth movements were assessed along the x (transverse), y (sagittal), and z (vertical) axes, and Von Mises stress distributions were analyzed in surrounding structures. In the MKS group, the first scenario showed the greatest molar crown displacement, while the third had the highest root-level movement. The ninth MT scenario yielded the most palatal crown displacement of incisors, while the sixth IZC scenario showed the greatest root movement. Apical force applications (MKS and IZC at 3–6 mm) allowed controlled displacement suited for Class II Division 2 malocclusions. In contrast, archwire-level and MT scenarios produced patterns favorable for Class II Division 1 cases. Anchorage type and force direction significantly affected distalization outcomes.