This work was financed by the International Align Research Award Program (Align Technology Inc., Tempe, AZ). However, the funding source had no involvement in the conduct of the research and/or preparation of the article. We would like to thank Dr. Sandra Tai and Dr. Samuel Tam for their generous support for providing the Invisalign cases and Nikolas Krstic for his professional support for statistic analyses.

This study received ethical approval from the Institutional Review Board at the University of British Columbia (No. H19-00787). To uphold confidentiality, all samples utilized in the study underwent de-identification procedures. Furthermore, prior to their inclusion in the research, informed consent was appropriately obtained from all participating patients.
NOTE: Each participant contributed four maxillary digital models, which encompassed the following:
<ol>
	<li>Pre-treatment digital model, with the palate scanned using iTero</li>
	<li>Post-treatment digital model, with the palate scanned using iTero</li>
	<li>Pre-treatment model, exported from the planning software.</li>
	<li>Predicted model, exported from the planning software.</li>
</ol>
This protocol leveraged a combination of several software tools, which included CloudCompare, Meshmixer, and Rhinoceros. These software platforms played a pivotal role in facilitating the registration process and enabling the segmentation of individual teeth for the purpose of analyzing their movements and orientations. It is worth noting that these software tools may be replicable with other open-source software options, provided that they can achieve similar objectives. A workflow illustrating the software sequence is presented in Figure 1.
1. Preparation
<ol>
	<li>Obtain the initial and final (predicted) models as stereolithographic (STL) files from the planning software by clicking on Tools &#62; Export &#62; STL. 
	NOTE: Models exported from the planning software present only clinical crowns and virtual gingiva without the palate.</li>
	<li>Obtain the pre-treatment and post-treatment digital models as STL files from the scanned model software (OrthoCAD) by selecting the scan, clicking and choosing Export &#62; Export Type (open shell), Data Format (File per Arch [arches oriented in occlusion]). 
	&#8203;NOTE: Models exported from the model scanning software include not only dentition but also gingiva and the whole palate.</li>
</ol>
2. Palatal superimposition of pre- and post-treatment digital models in CloudCompare
<ol>
	<li>Open the software and drag and drop the STL files of the pre-treatment and post-treatment digital models.</li>
	<li>Select each model and click Edit &#62; Colors &#62; Set Unique to change the colors of the selected models.</li>
	<li>Select the post-treatment digital model and click the Translate/Rotate icon. Right-click to drag the model so they are side by side. Click the Green Checkmark.</li>
	<li>Select the pre-treatment digital model and click the Segment icon.</li>
	<li>Click four points on the palatal rugae and right-click to deselect. Click Segment In, then click the Green Checkmark. Repeat steps 2.4 to 2.5 for the post-treatment digital model.</li>
	<li>Hide the PostTreatModel.remaining and PreTreatModel.remaining models and select both the PostTreatModel.part and PreTreatModel.part models.</li>
	<li>Click the Rough Registration alignment icon (point pair picking) and place at least three corresponding landmarks on the palate on each side of the midline for both the pre- and post-treatment palates. Click Align, then click the Green Checkmark.</li>
	<li>Unhide the meshes for both models and move the untransformed PostTreatModel.remaining model by copying the transformation matrix, clicking Edit &#62; Apply Transformation, and pasting the transformation matrix. 
	NOTE: The transformation matrix outputs are shown in the console.</li>
	<li>Hide the PostTreatModel.remaining and PreTreatModel.remaining models and select the PostTreatModel.part and PreTreatModel.part models.</li>
	<li>Click the Fine Registration alignment icon and make sure the PreTreatModel.part model is selected as the reference. Click Ok. 
	NOTE: Confirm the resulting root mean square (RMS) in the registration information window. A deviation of &#8804; 0.05 RMS is acceptable.</li>
	<li>Unhide the meshes for both models and move the untransformed PostTreatModel.remaining model by copying the transformation matrix, clicking Edit &#62; Apply Transformation, and pasting the transformation matrix.</li>
	<li>Save the superimposed PostTreatModel.remaining and PreTreatModel.remaining models as STL files.</li>
</ol>
3. Software-model preparation for superimposition with Rhinoceros software
<ol>
	<li>Import the STL files of the planning software pre-treatment and predicted models separately. 
	NOTE: When importing software models into measurement software such as Rhinoceros or CloudCompare, the orientation and models&#39; registration are preserved</li>
	<li>Select the simulated gingiva and press Delete to remove it.</li>
	<li>Click MeshTools, select Meshplane. Draw a plane around the teeth and move the plane to the occlusal 1/3 of the tooth crowns. This wil improve the superimposition precision.</li>
	<li>Double click the button Right to expand the right view.</li>
	<li>Enter the command MeshBooleanSplit and select the plane and all the teeth then press Enter.</li>
	<li>Delete the plane and cervical portions of the teeth leaving the 1/3 occlusal tooth crowns.</li>
	<li>Save the split model as an STL file.</li>
	<li>Repeat all steps for the other model.</li>
</ol>
4. Superimposition of software-predicted and post-treatment digital models with CloudCompare
<ol>
	<li>Drag and drop the STL files of the previously palatally superimposed pre-treatment and post-treatment digital models, and the split pre-treatment and split predicted models.</li>
	<li>Select each model and click Edit &#62; Colors &#62; Set Unique to change the colors of the selected models.</li>
	<li>Select both the pre-treatment and post-treatment digital models and click the Translate/Rotate icon. Right-click to drag the models so they are side by side.</li>
	<li>Request the software hide the split predicted model and post-treatment digital model by uncheckmarking the corresponding boxes. Select the split pre-treatment model and pre-treatment digital model.</li>
	<li>Click the Rough Registration alignment icon and place corresponding landmarks on the crowns&#39; cusps on both the split pre-treatment model and the pre-treatment digital model. Click Align, then click the Green Checkmark.</li>
	<li>Unhide the split predicted model and post-treatment model and move the untransformed post-treatment model by copying the transformation matrix, clicking Edit &#62; Apply Transformation, and pasting the transformation matrix.</li>
	<li>Hide the post-treatment and split predicted models. Select the pre-treatment and split pre-treatment models. Click the Fine Registration alignment icon for the best fit between the split pre-treatment model and the pre-treatment digital model.</li>
	<li>Unhide the meshes and move the untransformed model by copying the transformation matrix, clicking Edit &#62; Apply Transformation, and pasting the transformation matrix.</li>
	<li>Unhide the split predicted and post-treatment digital models, then Hide the split pre-treatment model and pre-treatment digital model to display the superimposition (Figure 2).</li>
	<li>Save the models as STL files.</li>
</ol>
5. Crown segmentation using Meshmixer
<ol>
	<li>Import the split predicted model and post-treatment digital model to Meshmixer.</li>
	<li>Click Edit &#62; Duplicate to duplicate the models for the number of teeth to be segmented. Label each model with the corresponding tooth number to be segmented.</li>
	<li>Hide the split predicted model by clicking the Eye icon, keeping the post-treatment digital model visible.</li>
	<li>On the post-treatment model, click Select and adjust the size of the Brush. To segment the selected crown, drag the Brush tool on the occlusal surface of the selected tooth, paying close attention to the cusp tips.</li>
	<li>Click Modify &#62; Invert, then Edit &#62; Discard to delete the rest of the model, leaving the segmented crown.</li>
	<li>Unhide the split predicted model and hide the post-treatment model by clicking the corresponding Eye icons.</li>
	<li>Repeat steps 5.4-5.5 for the split predicted model.</li>
	<li>Export each selected crown as STL files.</li>
	<li>Repeat all steps for each tooth segmentation.</li>
</ol>
6. Dental superimposition with CloudCompare
<ol>
	<li>Import the segmented post-treatment digital crowns and split software-predicted crowns into the software. Ensure that the orientation and cloud registration remain consistent. Establish the World Coordinate Grid to standardize the orientation of both right and left teeth, enhancing the reliability of the methodology. The grid&#39;s center should represent the (0,0,0,0,0,0) coordinate of the CloudCompare software cloud.</li>
	<li>Select both crowns and click Edit &#62; Normals &#62; Compute &#62; Per-Vertex.</li>
	<li>Select each tooth and click Edit &#62; Colors &#62; Set Unique to change the colors of the selected models.</li>
	<li>Hide the post-treatment tooth by unchecking the box and select both the hidden post-treatment tooth and the visible predicted tooth.</li>
	<li>Select the bottom view, click the Translate/Rotate icon, and use the planes X, Y, and Z to rotate the tooth such that the buccal cusp lines up with the vertical line.</li>
	<li>Select the left side view, click the Translate/Rotate icon, and line the buccal and lingual cusps with the horizontal line.</li>
	<li>Select the back view, click the Translate/Rotate icon, and line the buccal and lingual cusps with the horizontal line. 
	NOTE: Aim to align its occlusal and facial surfaces with the world axes and planes. Ensure the bounding box center of the tooth is positioned at the world origin. By adhering to the World Coordinate Grid, the positions of all teeth will be standardized. This step ensures a consistent and accurate conversion of X, Y, and Z translations across all axes, irrespective of an individual tooth&#39;s specific position.</li>
	<li>Once all the cusps line up, click the Translate/Rotate icon to center the tooth on the grid in all views.</li>
	<li>Unhide the post-treatment tooth and select the predicted tooth and post-treatment tooth.</li>
	<li>Click the Fine Registration alignment icon to register the post-treatment tooth over the predicted tooth. Click OK. 
	NOTE: Upon completion, CloudCompare will show the registration information, including superimposition RMS (Figure 3).</li>
	<li>To determine the positional and rotational differences between the two teeth, select the post-treatment tooth, copy the transformation matrix, click Edit &#62; Apply Transformation, and paste the transformation matrix.</li>
	<li>Select the Euler Angles icon to display the rotational and linear movements between the predicted tooth and the post-treatment tooth.</li>
	<li>Document all translation and rotation measurements in a spreadsheet. Repeat this process for all remaining posterior teeth. 
	NOTE: Use the American Board of Orthodontics (ABO) model grading system12 to identify clinically significant measurement differences. Differences greater than 0.5 mm linearly and 2 degrees angularly are considered clinically relevant.</li>
	<li>Adjust measurement values for the anterior-posterior direction of right-side teeth in a spreadsheet. This adjustment accounts for the standardized orientation of right-side teeth to left-side teeth.</li>
</ol>
7. Measurement specifications
<ol>
	<li>Understand the sequence of rotation and measurement conventions: CloudCompare employs the Tait-Bryan ZYX extrinsic (world origin) convention for its measurements. 
	NOTE: For translation, the axes represent X (buccolingual direction), Y (mesiodistal direction), and Z (vertical direction: intrusion/extrusion). Angular movements are represented by the X-axis (Psi - mesiodistal tipping), the Y-axis (Theta - buccolingual torque), and the Z-axis (Phi - mesiodistal rotation)22. Tooth movements are expressed in terms of the tooth&#39;s anatomy, regardless of its position in the arch. The sign of measures (+, -) indicates the direction from the world origin and rotation around its axes.</li>
	<li>Importance of contextual relevance: Note that the directional terms describing tooth movements (e.g., mesial, distal, buccolingual) reference the specific tooth and do not account for alterations relative to the dental arch.</li>
</ol>
8. Statistical analysis
<ol>
	<li>Use the R statistical package (v 3.2.3, RStudio Inc.) via RStudio (version 1.4.1103) for all the analyses.</li>
	<li>Select 32 teeth at random and perform duplicate measurements at a 1 month interval.</li>
	<li>Test intra-examiner reliability with Intra-class correlation coefficients (ICC) and Bland Altman analyses for both sets of measurements.</li>
	<li>Apply Hotelling&#39;s T-squared tests to test mean prediction differences between the predicted and achieved tooth positions for both angular and linear parameters.</li>
	<li>Adjust for multiple tooth comparisons using a Bonferroni correction on p-values, aiming for a family-wise error rate of 0.05.</li>
	<li>Conduct a post-hoc Hotelling&#39;s T-squared test if any significant differences are detected to determine if prediction differences for each tooth type and movement parameter are significant. Consider discrepancies of 0.25 mm or more in linear measurements and 2&#176; or above for angular measurements as clinically relevant.</li>
</ol>

Evaluation of Maxillary Posterior Tooth Movement Using Digital Superimposition

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Available from: <a target="_blank" href="https://commons.wikimedia.org/wiki/Tait-Bryan_angles">https://commons.wikimedia.org/wiki/Tait-Bryan_angles</a> (2023)</li><li>Al-Nadawi, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effect+of+clear+aligner+wear+protocol+on+the+efficacy+of+tooth+movement.">Effect of clear aligner wear protocol on the efficacy of tooth movement.</a> Angle Orthodontist. 91 (2), 157-163 (2021).</li><li>Cortona, A., Rossini, G., Parrini, S., Dergibus, A., Castroflorio, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Clear+aligner+orthodontic+therapy+of+rotated+mandibular+round-shaped+teeth:+A+finite+element+study.">Clear aligner orthodontic therapy of rotated mandibular round-shaped teeth: A finite element study.</a> Angle Orthodontist. 90 (2), 247-254 (2020).</li><li>Nucera, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effects+of+composite+attachments+on+orthodontic+clear+aligners+therapy:+A+systematic+review.">Effects of composite attachments on orthodontic clear aligners therapy: A systematic review.</a> Materials. 15 (2), 533 (2022).</li></ol>

The authors have nothing to disclose.

The palatal rugae have a unique configuration at adolescence; they remain constant during growth, are authentic markers for personal identification, and are considered stable anatomic references for maxillary model superimposition24,25,26,27. Dai et al. used this method to compare the achieved and predicted tooth movement of maxillary first molars and central incisors with clear aligners after the first premolar extraction28. The achieved post-treatment model was registered via Rapidform software to the pre-treatment and the planned post-treatment model. They reported statistically significant differences between the predicted and achieved tooth movements. The pre- and post-treatment models were acquired from different sources (alginate impressions and intraoral scans). The measurements of tooth movement were expressed within a coordinate system using the posterior occlusal plane as a transverse plane and the palatal suture as a guide to constructing a midsagittal plane. Since angular and translational parameters for the respective maxillary first molars and upper central incisors were projected onto these planes, it is difficult to compare their results with investigations using different coordinate systems (e.g., a coordinate origin based on a tooth&#39;s approximate center of resistance)17,18,19,20,21.
The study was limited to the maxillary arch so the palate and its rugae could be used to register the predicted and achieved models. Previously, model registration has been done using untreated or assumedly stable posterior teeth16,17,18,19, best-fit algorithms20,21, miniscrews26,27, tori29, implants30, the cranial-base31, or other bony structures32. In one of the few previous studies using palatal superimposition to assess clear aligner efficacy, Dai et al. compared the achieved and predicted tooth movements of maxillary first molars and central incisors in extraction cases28, although they did not report individual differences in tooth position and orientation with the six degrees of freedom used in this and other studies20,21.
Anterior teeth were not included in this protocol because the shapes of their clinical crowns were too difficult to orient within the world coordinate system used by CloudCompare to express its transformations. However, the premolars and molars had sufficient occlusal landmarks and facial surface regularity to allow each treated tooth&#39;s occlusal surface, long axis, and bounding box to be oriented to the software&#39;s origin and world coordinates.
Using a transformation matrix to provide the translational and rotational information describing tooth movement during registration requires adherence to specific conventions. CloudCompare employs the Tait-Bryan convention, in which a ZYX axis rotation sequence is adopted first, followed by the 3D translations from world zero needed to complete the match. As the angles reported in this study reflect the Tait-Bryan convention33, studies using different conventions would produce different results. Aligning the post-treatment tooth to the world origin and coordinates ensured the measurements indicated translation from the tooth&#39;s original position and directions determined by each tooth&#39;s specific surface anatomy.
Overall, our results showed that the rotation and torque of the achieved tooth position were statistically and clinically significantly different from the predictions, with more mesial rotation and buccal torque after treatment with clear aligners. While rotational movement in maxillary first molars was relatively successful, de-rotation&#160;in the first and second premolars was more problematic, suggesting the morphology of premolar crowns might contribute to this difference. These findings are similar to a recent study conducted by Al-Nadawia&#160;et al., who found that the accuracy of posterior teeth movements with a 7-day protocol is not as accurate as the 14 days protocol for intrusion, distal intrusion, distal-crown tip, and buccal-crown torque34. In order to determine whether the accuracy of Invisalign had improved with the newer technology, Haouili et al. updated Kravitz et al.&#39;s pioneering study17 and they also found the least overall accuracy with rotation, particularly challenging for canines, premolars, and molars 21.
Translation data showed no statistically significant differences for all three directions, in agreement with previous studies. Simon et al. found that upper molar distalization was the most effectively predicted movement18. A non-statistical but clinically significant prediction difference was noticed for occlusal-gingival movement in the first molars in the current study, which tended to be slightly intruded relative to their predicted positions. Haouili et al. also indicated that although the extrusion of the maxillary incisors improved with the use of optimized extrusion attachments, extrusion of the maxillary and mandibular molars had the lowest accuracy21.
In the present study, tooth movements with attachments were not different from teeth with no attachments in achieving the desired tooth movements. Tooth movements with optimized attachment appeared to be slightly inaccurate with rotational movement. Although teeth with optimized attachment showed more accurate torque tooth movement compared with conventional or no attachments, the overall torque tooth movements were challenging. Kravitz et al. stated that attachments may provide minute clinical improvements with rotational movements compared to no attachments; however, not with a statistical significance16. On the other hand, Simon et al. found that attachments are significantly beneficial when derotating premolars18. Cortona et al. also addressed with a finite study that the most efficient way to derotate mandibular round-shaped teeth was to add a single attachment with a 1.2&#176; of aligner activation35. Nucera et al. systematically reviewed the effects of composite attachments on clear aligner therapy and outlined the contradictory results in the current literature36. The lack of evidence warrants further clinical trials to clarify the influence of attachments and their number, size, shape, and position on each orthodontic movement.
Overall, Invisalign achieved most of its predicted posterior tooth movements in adolescents and adults with mild to moderate malocclusions. Specifically, the predicted de-rotation of the maxillary premolars, and especially the first premolar, was more challenging. All maxillary posterior teeth tended to torque&#160;buccally without adequate torque control. The more distally positioned the tooth, the more unpredictable the result. With or without attachments or different types of attachments seemed to make no difference in the prediction. Generally, additional refinements or overcorrection would be necessary to achieve all predictions. Comparisons of Invisalign-predicted with clinically achieved&#160;digital models in the maxillary arch can benefit from model registration using both palatal and dental features, individual tooth segmentation, and the mathematical transformations used to match them.

In 1999, digitally fabricated removable orthodontic appliances were made commercially available by Align (Align Technology Inc., Tempe, AZ). Originally, this system was designed to solve non-growing cases with mild to moderate crowding or close small spaces as an aesthetic alternative to traditional fixed edgewise appliances. With decades of improvements in computer-aided design and manufacturing (CAD/CAM), dental materials, and treatment planning, clear aligner therapy (CAT) has since been used to treat over 10 million patients with various malocclusions worldwide1. A recent retrospective study suggested that CAT is as effective as fixed appliance therapy for the teenage population with mild malocclusions, with significantly improved results in tooth alignment, occlusal relations, and overjet2. The number of appointments, emergency visits, and overall treatment time also had better outcomes for clear aligner therapy patients. Although CAT can be used to treat non-extraction, mild-to-moderate malocclusions in non-growing patients3,4, and shorten treatment duration and chair time5, it remains unclear whether the treatment is as effective as the gold standard of conventional labial braces4,6,7,8,9, especially for anteroposterior and vertical correction10.
ClinCheck is a software platform developed by Align to provide clinicians with virtual three-dimensional (3D) simulations of prospective tooth movements. Primarily concerned with the patient&#39;s initial status and the clinician&#39;s prescribed treatment plan, it can also be a visual communication tool for the patient. Any mismatch between the predicted and achieved results may require a mid-course correction, refinement, or conversion to fixed appliance therapy. Consequently, the reliability of software predictions has drawn increasing attention from investigators. Since Lagravere and Flores-Mir&#39;s systematic review published in 200511, investigations into the concordance between predicted models and post-treatment models have been measured in different ways, measurement methods including arch-length, inter-canine distance, overbite, overjet, midline deviation12, the American Board of Orthodontics objective grading system (ABO-OGS) reduction score13, upper and lower interdental width14, and measures derived from cone-beam computed tomography15.
Comparisons have also been made by superimposing 3D models16,17,18,19,20,21. For example, many current software platforms, such as ToothMeasure (internal software developed by Align Technology), can reproducibly superimpose two digital models using user-selected reference points on untreated teeth, palatal rugae, or dental implants. Since the predicted and achieved models usually do not include the palatal surfaces, many previous studies15,16,17,18 have used the untreated posterior teeth as references for superimposition, including the possibility of adding errors due to the relative movements of these teeth. These studies have been confined to anterior regions of the arch in relatively simple cases with spacing or mild to moderate crowding.
Gr&#252;nheid et al. used mathematical superimposition to quantify the discrepancies between virtual treatment plans and actual treatment outcomes to evaluate the accuracy of full-dentition CAT without stable anatomic structures in digital models20. Haouili et al. used the same method in a best-fit algorithm within the Compare software to conduct a prospective follow-up study on the efficacy of tooth movement with CAT21. The aim was to provide an update on the accuracy associated with emerging technology, i.e., SmartForce, SmartTrack aligner materials, and digital scans. Their findings of an improved overall accuracy from 41%17 to 50%21&#160;were encouraging but do not negate the possibility that some tooth movements are still not satisfactorily achievable with the clear aligner system.
When predicted and achieved, digital models include a common 3D reference independent of the dentition, such as palatal rugae, dental implants, or tori; they can be co-registered within the coordinate system of many suitable software platforms. If a tooth of interest is then segmented from one and transformed mathematically to match its displaced version in the other, the transformation matrix contains the complete information needed to describe the entire 3D transposition. Its content can be expressed as three translations and three rotations described by a formal convention. An example is found on the Invisalign ClinCheck Pro 3D control software, where the numerical parameters indicating the 3D tooth movements needed to move teeth to their predicted positions are shown in a tooth movement table.
While the initial and final (predicted) models from the planning software share a common coordinate system provided by the same software platform, their absence of palates restricts the possibility of co-registering with any other digital dentition model unless they possess identical dentition. In this context, it was hypothesized that the superimposition of software-predicted and post-treatment (achieved) models would be feasible. This feasibility arises from the availability of two pairs: initial and final (automatically superimposed during export from planning software) and another pair of pre-treatment and achieved models (superimposed using palatal rugae). These pairs could be registered using the pre-treatment dentition as a reference to align them with the Invisalign-initial model. Subsequently, the segmentation of individual teeth could be performed to assess differences in their positions and orientations. This assessment involves transposing teeth between the models, and the transformation matrices would enable a numerical quantification of the translations and re-orientations.
In this protocol, an approach to evaluate the effectiveness of CAT in addressing mild to moderate malocclusions in both adolescents and adults was introduced, specifically focusing on the maxillary posterior teeth. The null hypothesis was that there was no difference between the achieved and the planning software-predicted tooth position in the maxillary posterior teeth after the first series of clear aligners.

<table><tbody><tr><td>CloudCompare&amp;nbsp;</td><td>GPL software&amp;nbsp;</td><td>&amp;nbsp;Version 2.11</td><td>open-source software (https://www.cloudcompare.net/)</td></tr><tr><td>Meshmixer software&amp;nbsp;</td><td>Autodesk, Inc.</td><td></td><td></td></tr><tr><td>Rhinoceros 5.0&amp;nbsp;</td><td>Robert McNeel &amp;amp; Associates</td><td>Version 5.0</td><td></td></tr></tbody></table>

measuring maxillary posterior tooth movement: a model assessment using palatal and dental superimposition

Since the introduction of Invisalign by Align Technology, Inc. in 1999, questions and debates have persisted regarding the precision of Invisalign (clear aligner) therapy, particularly when compared to the use of traditional fixed appliances. This becomes particularly significant in cases involving anteroposterior, vertical, and transverse corrections, where precise comparisons are of paramount importance. To address these inquiries, this study introduces a meticulously devised protocol, placing a primary emphasis on digitally superimposing the movement of maxillary posterior teeth to facilitate accurate analysis. The sample included 25 patients who had completed their first series of Invisalign (clear) aligners. Four maxillary digital models (pre-treatment, post-treatment, ClinCheck-initial, and final models) were digitally superimposed using the palate rugae and dentitions as stable references. A software combination was used for model superimposition and tooth segmentation. Transformation matrices then expressed the differences between the achieved and predicted tooth positions. Thresholds for clinically relevant differences were at &plusmn;0.25 mm for linear displacement and &plusmn;2&#176; for rotation. Differences were assessed using Hotelling's T-squared tests with Bonferroni correction. The mean differences in rotation (2.036&#176; &plusmn; 4.217&#176;) and torque (-2.913&#176; &plusmn; 3.263&#176;) were significant statistically and clinically, with p-values of 0.023 and 0.0003 respectively. De-rotation of premolars and torque control for all posterior teeth were less predictable. All mean differences for the linear measurements were statistically and clinically insignificant, except that the first molars seemed slightly (0.256 mm) more intruded than their predicted position. The clear aligner system appears to meet its prediction for most translational tooth movements and mesial-distal tipping in maxillary posterior teeth for non-extraction cases with mild to moderate malocclusions.

A minimum sample size of 24 cases was required to detect an effect change of 0.6&#176; for the average tip and torque angles, with an 80% power and an alpha of 0.0523. The inclusion criteria were as follows: (1) full permanent dentition through the first molars, (2) Class I malocclusions, or less than 2 mm Class II /III malocclusions with spacing, or mild to moderate crowding that had undergone non-extraction Invisalign treatment, (3) completion of the first series of Invisalign aligners at least, and (4) palatal rugae presented on both the initial and refinement intra-oral scans. The exclusion criteria were: (1) previous exposure to auxiliary expansion and distalization appliances, (2) visible wear facets in the dentition during treatment, (3) history of trauma, craniofacial syndrome, or missing teeth, and (4) poor compliance with aligner wearing documented on the chart. The second molars of several cases were absent or erupting and therefore excluded from the analysis. Accordingly, this study comprised 150 teeth (50 first premolars, 50 second premolars, and 50 first molars) selected from 25 participants (17 females and 8 males), aged from 12 to 44 years old with a mean age of 24.8 &#177; 8.8 years. Of the 25 patients, 4 were Class I, 15 were Class II, and 6 were Class III malocclusions, all less than 2 mm. The average number of trays was 24.8 &#177; 11.2, and the average treatment duration was 214 &#177; 131 days. Among the 150 maxillary posterior teeth, there were 63 teeth without any attachment, 7 with conventional, and 80 with optimized attachments.
Mean ICCs for intra-examiner reliability were greater than 0.990, suggesting the intra-examiner agreement was excellent (Table 1). The results of Bland-Altman analyses are reported in Table 2, which also showed high intra-examiner agreement.
Table 3 shows the angular and linear differences between the predicted and achieved tooth position in maxillary posterior teeth. In general, the angular measures for rotation, torque, and tip had notably greater variation than the distance measures for buccal-lingual, mesial-distal, and occlusal-gingival translations. The mean rotation differences for first premolars and second premolars were greater than 2&#176; and the 95% confidence interval did not include zero. This suggests that clinically, the maxillary first and second premolars were significantly rotated mesially. Torque for all tooth types substantially deviated from zero, while the mean difference for second premolars and first molars was less than -2&#176;, suggesting all maxillary posterior teeth, especially the second premolars and first molars, had a more clinically relevant buccal crown torque relative to the predicted position.
Hotelling&#39;s T-squared test results and the 95% overall confidence intervals with Bonferroni correction for each parameter are presented in Table 4. The results indicate the mean differences in rotation (2.036&#176; &#177; 4.217&#176;) and torque (-2.913&#176; &#177; 3.263&#176;) were statistically significantly different from zero, with p-values of 0.023 and 0.0003 respectively.
To further explore the possible effects of attachment use on the accuracy of the prediction, a primary investigation can be visualized in Figure 4, which showed minor differences across different attachments (No, conventional, or optimized attachment). However, this is likely due to low frequencies of conventional attachment.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65531/65531fig01.jpg" /> 
Figure 1: A workflow of the software usage sequence. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65531/65531fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/65531/65531fig02.jpg" /> 
Figure 2: Superimposition of the software-final (predicted) and the post-treatment (achieved) models. (A)&#160;Four models from one subject registered in the same coordinate system. Color-coding indicates the pre-treatment and post-treatment models with palates but different dentitions, the software-initial model without a palate and the same dentition as the pre-treatment model, and the softwarefinal model without a palate and the predicted dentition. The method for superimposition is described in the text. (B) The software-predicted final and post-treatment models shown alone. Differences in their tooth positions and orientations were measured in this study. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65531/65531fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/65531/65531fig03.jpg" /> 
Figure 3: Dental superimposition with measurements. (A) A segmented first molar from the achieved (post-treatment) model registered to the software-predicted version. The transformation matrix for registration and the root mean square (RMS) of the fit is from CloudCompare&#39;s pop-up window. (B) Euler angles and displacements derived from the transformation matrix. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65531/65531fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/65531/65531fig04.jpg" /> 
Figure 4: Comparison of prediction differences without attachment with conventional and optimized attachments. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65531/65531fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Parameter</td>
			<td>Mean</td>
			<td>95%</td>
			<td>CI</td>
			<td>Significance</td>
		</tr>
		<tr>
			<td>Rotation (&#176;)</td>
			<td>1</td>
			<td>1</td>
			<td>1</td>
			<td>0</td>
		</tr>
		<tr>
			<td>Torque (&#176;)</td>
			<td>0.991</td>
			<td>0.982</td>
			<td>0.996</td>
			<td>0</td>
		</tr>
		<tr>
			<td>Tip (&#176;)</td>
			<td>0.992</td>
			<td>0.983</td>
			<td>0.996</td>
			<td>0</td>
		</tr>
		<tr>
			<td>Buccal-Lingual (mm)</td>
			<td>0.999</td>
			<td>0.997</td>
			<td>0.999</td>
			<td>0</td>
		</tr>
		<tr>
			<td>Mesial-Distal (mm)</td>
			<td>0.99</td>
			<td>0.979</td>
			<td>0.995</td>
			<td>0</td>
		</tr>
		<tr>
			<td>Occlusal-Gingival (mm)</td>
			<td>0.998</td>
			<td>0.996</td>
			<td>0.999</td>
			<td>0</td>
		</tr>
	</tbody>
</table>
Table 1: Intra-class correlation coefficients (ICC) for intra-examiner reliability (n = 32 teeth). CI: confidence interval.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Parameter</td>
			<td>Mean difference</td>
			<td>95%</td>
			<td>CI</td>
		</tr>
		<tr>
			<td>Rotation (&#176;)</td>
			<td>0.032</td>
			<td>-0.045</td>
			<td>0.137</td>
		</tr>
		<tr>
			<td>Torque (&#176;)</td>
			<td>0.182</td>
			<td>-0.099</td>
			<td>0.503</td>
		</tr>
		<tr>
			<td>Tip (&#176;)</td>
			<td>0.061</td>
			<td>-0.08</td>
			<td>0.218</td>
		</tr>
		<tr>
			<td>Buccal-Lingual (mm)</td>
			<td>-0.011</td>
			<td>-0.043</td>
			<td>0.012</td>
		</tr>
		<tr>
			<td>Mesial-Distal (mm)</td>
			<td>0.008</td>
			<td>-0.033</td>
			<td>0.048</td>
		</tr>
		<tr>
			<td>Occlusal-Gingival (mm)</td>
			<td>0.011</td>
			<td>-0.002</td>
			<td>0.026</td>
		</tr>
	</tbody>
</table>
Table 2: Results of Bland-Altman analyses for intra-examiner agreement (n = 32 teeth). CI: confidence interval.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td rowspan="2" style="min-width: 60px;">Para-meter</td>
			<td colspan="4" style="min-width: 60px;">First Premolar (n=50)</td>
			<td colspan="4" style="min-width: 60px;">Second Premolar (n=50)</td>
			<td colspan="4" style="min-width: 60px;">First Molar (n=50)</td>
		</tr>
		<tr>
			<td style="min-width: 60px;">Mean</td>
			<td style="min-width: 60px;">SD</td>
			<td style="min-width: 60px;">95%</td>
			<td style="min-width: 60px;">CI</td>
			<td style="min-width: 60px;">Mean</td>
			<td style="min-width: 60px;">SD</td>
			<td style="min-width: 60px;">95%</td>
			<td style="min-width: 60px;">CI</td>
			<td style="min-width: 60px;">Mean</td>
			<td style="min-width: 60px;">SD</td>
			<td style="min-width: 60px;">95%</td>
			<td style="min-width: 60px;">CI</td>
		</tr>
		<tr>
			<td style="min-width: 60px;">Rotation (&#176;)</td>
			<td style="min-width: 60px;">2.801</td>
			<td style="min-width: 60px;">3.881</td>
			<td style="min-width: 60px;">1.767</td>
			<td style="min-width: 60px;">4.023</td>
			<td style="min-width: 60px;">2.472</td>
			<td style="min-width: 60px;">5.265</td>
			<td style="min-width: 60px;">1.195</td>
			<td style="min-width: 60px;">4.148</td>
			<td style="min-width: 60px;">0.835</td>
			<td style="min-width: 60px;">3.004</td>
			<td style="min-width: 60px;">0.098</td>
			<td style="min-width: 60px;">1.74</td>
		</tr>
		<tr>
			<td style="min-width: 60px;">Torque (&#176;)</td>
			<td style="min-width: 60px;">-1.261</td>
			<td style="min-width: 60px;">1.912</td>
			<td style="min-width: 60px;">-1.765</td>
			<td style="min-width: 60px;">-0.722</td>
			<td style="min-width: 60px;">-3.597</td>
			<td style="min-width: 60px;">3.586</td>
			<td style="min-width: 60px;">-4.588</td>
			<td style="min-width: 60px;">-2.512</td>
			<td style="min-width: 60px;">-3.881</td>
			<td style="min-width: 60px;">3.413</td>
			<td style="min-width: 60px;">-4.895</td>
			<td style="min-width: 60px;">-2.934</td>
		</tr>
		<tr>
			<td style="min-width: 60px;">Tip (&#176;)</td>
			<td style="min-width: 60px;">0.746</td>
			<td style="min-width: 60px;">2.851</td>
			<td style="min-width: 60px;">-0.079</td>
			<td style="min-width: 60px;">1.632</td>
			<td style="min-width: 60px;">0.409</td>
			<td style="min-width: 60px;">3.015</td>
			<td style="min-width: 60px;">-0.434</td>
			<td style="min-width: 60px;">1.238</td>
			<td style="min-width: 60px;">-0.326</td>
			<td style="min-width: 60px;">1.917</td>
			<td style="min-width: 60px;">-0.582</td>
			<td style="min-width: 60px;">0.506</td>
		</tr>
		<tr>
			<td style="min-width: 60px;">Buccal-Lingual (mm)</td>
			<td style="min-width: 60px;">-0.18</td>
			<td style="min-width: 60px;">0.455</td>
			<td style="min-width: 60px;">-0.311</td>
			<td style="min-width: 60px;">-0.046</td>
			<td style="min-width: 60px;">-0.156</td>
			<td style="min-width: 60px;">0.516</td>
			<td style="min-width: 60px;">-0.307</td>
			<td style="min-width: 60px;">-0.018</td>
			<td style="min-width: 60px;">-0.048</td>
			<td style="min-width: 60px;">0.619</td>
			<td style="min-width: 60px;">-0.203</td>
			<td style="min-width: 60px;">0.132</td>
		</tr>
		<tr>
			<td style="min-width: 60px;">Mesial-Distal (mm)</td>
			<td style="min-width: 60px;">0.143</td>
			<td style="min-width: 60px;">0.535</td>
			<td style="min-width: 60px;">-0.006</td>
			<td style="min-width: 60px;">0.309</td>
			<td style="min-width: 60px;">0.155</td>
			<td style="min-width: 60px;">0.56</td>
			<td style="min-width: 60px;">-0.01</td>
			<td style="min-width: 60px;">0.299</td>
			<td style="min-width: 60px;">0.213</td>
			<td style="min-width: 60px;">0.618</td>
			<td style="min-width: 60px;">0.041</td>
			<td style="min-width: 60px;">0.392</td>
		</tr>
		<tr>
			<td style="min-width: 60px;">Occlusal-Gingival (mm)</td>
			<td style="min-width: 60px;">-0.141</td>
			<td style="min-width: 60px;">0.407</td>
			<td style="min-width: 60px;">-0.256</td>
			<td style="min-width: 60px;">-0.031</td>
			<td style="min-width: 60px;">-0.206</td>
			<td style="min-width: 60px;">0.408</td>
			<td style="min-width: 60px;">-0.323</td>
			<td style="min-width: 60px;">-0.09</td>
			<td style="min-width: 60px;">-0.256</td>
			<td style="min-width: 60px;">0.398</td>
			<td style="min-width: 60px;">-0.363</td>
			<td style="min-width: 60px;">-0.147</td>
		</tr>
	</tbody>
</table>
Table 3: Descriptive statistics for angular and linear differences between the predicted and the achieved tooth position for maxillary first premolars, second premolars, and first molars. Positive values indicated an achieved tooth position more buccal, distal, or occlusal, or with more mesial rotation, more distal crown tip, or more lingual crown torque than the predicted tooth position. SD: standard deviation; CI: confidence interval.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Parameter</td>
			<td>Mean</td>
			<td>SD</td>
			<td>95%</td>
			<td>CI</td>
			<td>P</td>
		</tr>
		<tr>
			<td>Rotation (&#176;)</td>
			<td>2.036</td>
			<td>4.217</td>
			<td>1.408</td>
			<td>2.756</td>
			<td>0.023*</td>
		</tr>
		<tr>
			<td>Torque (&#176;)</td>
			<td>-2.913</td>
			<td>3.263</td>
			<td>-3.411</td>
			<td>-2.388</td>
			<td>0.0003*</td>
		</tr>
		<tr>
			<td>Tip (&#176;)</td>
			<td>0.374</td>
			<td>2.641</td>
			<td>-0.049</td>
			<td>0.8</td>
			<td>1</td>
		</tr>
		<tr>
			<td>Buccal-Lingual (mm)</td>
			<td>-0.128</td>
			<td>0.534</td>
			<td>-0.216</td>
			<td>-0.041</td>
			<td>0.186</td>
		</tr>
		<tr>
			<td>Mesial-Distal (mm)</td>
			<td>0.17</td>
			<td>0.569</td>
			<td>-0.076</td>
			<td>0.258</td>
			<td>1</td>
		</tr>
		<tr>
			<td>Occlusal-Gingival (mm)</td>
			<td>-0.201</td>
			<td>0.405</td>
			<td>-0.266</td>
			<td>-0.136</td>
			<td>0.123</td>
		</tr>
	</tbody>
</table>
Table 4: Comparisons of angular and linear mean prediction differences in all the maxillary posterior teeth measured with Hotelling&#39;s T-squared tests with Bonferonni correction. Positive values indicated an achieved tooth position more buccal, distal, or occlusal, or with more mesial rotation, more distal crown tip, or more lingual crown torque than the predicted tooth position. *P &#60; 0.05.

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Author Spotlight: 3D Movement Assessment of Maxillary Posterior Teeth in Clear Aligner Treatment

This manuscript presents a comprehensive protocol to evaluate the three-dimensional (3D) movement of maxillary posterior teeth with clear aligners using digital model superimposition, an invaluable tool in orthodontics and dentofacial orthopedics.

Measuring Maxillary Posterior Tooth Movement: A Model Assessment using Palatal and Dental Superimposition

Since the introduction of Invisalign by Align Technology, Inc. in 1999, questions and debates have persisted regarding the precision of Invisalign (clear ...

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Nanyang Technological University

Research

JoVE Journal

Medicine

1.0K Views.  King Abdulaziz University. Beoordeling van tandheelkundige bewegingen is belangrijk bij het evalueren van de resultaten van orthodontische behandelingen. We gaan een methode demonstreren om de driedimensionale beweging van maxillaire achterste tanden te evalueren wanneer ze een Clear Aligner-behandeling hebben ondergaan. Dit protocol biedt een combinatie van verschillende softwaretools, evenals palatinale rugae en gebit als stabiele referenties.Deze combinatie maakt het mogelijk om individuele tanden over elkaar...

Video: Auteur in de schijnwerpers: 3D-bewegingsbeoordeling van maxillaire posterieure tanden bij behandeling met clear aligner

In situ Compressive Loading and Correlative Noninvasive Imaging of the Bone-periodontal Ligament-tooth Fibrous Joint

This study demonstrates a novel biomechanics testing protocol. The advantage of this protocol includes the use of an in situ loading device coupled to a high resolution X-ray microscope, thus enabling visualization of internal structural elements under simulated physiological loads and wet conditions. Experimental specimens will include intact bone-periodontal ligament (PDL)-tooth fibrous joints. Results will illustrate three important features of the protocol as they can be applied to organ level biomechanics: 1) reactionary force vs. displacement: tooth displacement within the alveolar socket and its reactionary response to loading, 2) three-dimensional (3D) spatial configuration and morphometrics: geometric relationship of the tooth with the alveolar socket, and 3) changes in readouts 1 and 2 due to a change in loading axis, i.e. from concentric to eccentric loads. Efficacy of the proposed protocol will be evaluated by coupling mechanical testing readouts to 3D morphometrics and overall biomechanics of the joint. In addition, this technique will emphasize on the need to equilibrate experimental conditions, specifically reactionary loads prior to acquiring tomograms of fibrous joints. It should be noted that the proposed protocol is limited to testing specimens under ex vivo conditions, and that use of contrast agents to visualize soft tissue mechanical response could lead to erroneous conclusions about tissue and organ-level biomechanics.

In situ Compressive Loading and Correlative Noninvasive Imaging of the Bone-periodontal Ligament-tooth Fibrous Joint

In this study, the use of an in situ loading device coupled with micro-X-ray computed tomography for fibrous joint biomechanics will be discussed. Experimental readouts identifiable with an overall change in joint biomechanics will include: 1) reactionary force vs. displacement, i.e. tooth displacement within the alveolar socket and its reactionary response to loading, 2) three-dimensional (3D) spatial configuration and morphometrics, i.e.&#160;geometric relationship of the tooth with the alveolar socket, and 3) changes in readouts 1 and 2 due to a change in loading axis, i.e. concentric or eccentric loads.

This study demonstrates a novel biomechanics testing protocol. The advantage of this protocol includes the use of an in situ loading device coupled to a ...

Bioengineering

Rat Model of Photochemically-Induced Posterior Ischemic Optic Neuropathy

Posterior Ischemic optic neuropathy (PION) is a sight-devastating disease in clinical practice. However, its pathogenesis and natural history have&#160;remained poorly understood. Recently, we developed a reliable, reproducible animal model of PION and tested the treatment effect of some neurotrophic factors in this model1. The purpose of this video is to demonstrate our photochemically induced model of posterior ischemic optic neuropathy, and to evaluate its effects with retrograde labeling of retinal ganglion cells. Following surgical exposure of the posterior optic nerve, a photosensitizing dye, erythrosin B, is intravenously injected and a laser beam is focused onto the optic nerve surface. Photochemical interaction of erythrosin B and the laser during irradiation damages the vascular endothelium, prompting microvascular occlusion mediated by platelet thrombosis and edematous compression. The resulting ischemic injury yields a gradual but pronounced retinal ganglion cell dieback, owing to a loss of axonal input - a remote, injury-induced and clinically relevant outcome. Thus, this model provides a novel platform to study the pathophysiologic course of PION,&#160;and can be further optimized for testing therapeutic approaches for optic neuropathies as well as other CNS ischemic diseases.

The goal of this protocol is to photochemically induce ischemic injury to the posterior optic nerve in rat. This model is critical to studies of the pathophysiology of posterior ischemic optic neuropathy, and therapeutic approaches for this and other optic neuropathies, as well as of other CNS ischemic diseases.

Posterior Ischemic optic neuropathy (PION) is a sight-devastating disease in clinical practice. However, its pathogenesis and natural history ...

Analysis of Expansion Patterns from SARPE Using Finite Element Models

Finite Element Analysis Model for Assessing Expansion Patterns from Surgically Assisted Rapid Palatal Expansion

Surgically assisted rapid palatal expansion (SARPE) was introduced to release bony resistance to facilitate skeletal expansion in skeletally mature patients. However, asymmetric expansion between the left and right sides has been reported in 7.52% of all SARPE patients, of which 12.90% had to undergo a second surgery for correction. The etiologies leading to asymmetric expansion remain unclear. Finite element analysis has been used to evaluate the stress associated with SARPE in the maxillofacial structures. However, as a collision of the bone at the LeFort I osteotomy sites occurs only after a certain amount of expansion, most of the existing models do not truly represent the force distribution, given that the expansion amount of these existing models rarely exceeds 1 mm. Therefore, there is a need to create a novel finite element model of SARPE that could perform a clinically required amount of expander activation for further analysis of the expansion patterns of the hemimaxillae in all three dimensions. A three-dimensional (3D) skull model from cone beam computed tomography (CBCT) was imported into Mimics and converted into mathematical entities to segment the maxillary complex, maxillary first premolars, and maxillary first molars. These structures were transferred into Geomagic for surface smoothing and cancellous bone and periodontal ligament creation. The right half of the maxillary complex was then retained and mirrored to create a perfectly symmetrical model in SolidWorks. A Haas expander was constructed and banded to the maxillary first premolars and first molars. Finite element analysis of various combinations of buccal osteotomies at different angles with 1 mm clearance was performed in Ansys. A convergence test was conducted until the desired amount of expansion on both sides (at least 6 mm in total) was achieved. This study lays the foundation for evaluating how buccal osteotomy angulation influences the expansion patterns of SARPE.

Author Spotlight: Development of a Novel Finite Element Analysis Model for Improved Orthognathic Surgical Techniques 

A set of novel finite element models of surgically assisted rapid palatal expansion (SARPE) that could perform a clinically required amount of expander activation with various angles of buccal osteotomy was created for further analysis of the expansion patterns of the hemimaxillae in all three dimensions.

Surgically assisted rapid palatal expansion (SARPE) was introduced to release bony resistance to facilitate skeletal expansion in skeletally mature ...

A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible

Valid comparisons of postoperative accuracy results in computer-assisted reconstruction of the mandible are difficult due to heterogeneity in imaging modalities, mandibular defect classification, and evaluation methodologies between studies. This guideline uses a step-by-step approach guiding the process of imaging, classification of mandibular defects and volume assessment of three-dimensional (3D) models, after which a legitimized quantitative accuracy evaluation method can be performed between the postoperative clinical situation and the preoperative virtual plan. The condyles and the vertical and horizontal corners of the mandible are used as bony landmarks to define virtual lines in the computer-assisted surgery (CAS) software. Between these lines the axial, coronal, and both sagittal mandibular angles are calculated on both pre- and postoperative 3D models of the (neo)mandible and subsequently the deviations are calculated. By superimposing the postoperative 3D model to the preoperative virtually planned 3D model, which is fixed to the XYZ axis, the deviation between pre- and postoperative virtually planned dental implant positions can be calculated. This protocol continues and specifies an earlier publication of this evaluation guideline.

Here, we propose a practical, feasible and reproducible evaluation guideline for computer-assisted reconstruction of the mandible in order to create uniformity between studies regarding postoperative accuracy evaluation. This protocol continues and specifies an earlier publication of this evaluation guideline.

Valid comparisons of postoperative accuracy results in computer-assisted reconstruction of the mandible are difficult due to heterogeneity in imaging ...

3D Imaging of PDL Collagen Fibers during Orthodontic Tooth Movement in Mandibular Murine Model

Orthodontic tooth movement is a complex biological process of altered soft and hard tissue remodeling as a result of external forces. In order to understand these complex remodeling processes, it is critical to study the tooth and periodontal tissues within their 3D context and therefore minimize any sectioning and tissue artefacts. Mouse models are often utilized in developmental and structural biology, as well as in biomechanics due to their small size, high metabolic rate, genetics and ease of handling. In principle this also makes them excellent models for dental related studies. However, a major impediment is their small tooth size, the molars in particular. This paper is aimed at providing a step by step protocol for generating orthodontic tooth movement and two methods for 3D imaging of the periodontal ligament fibrous component of a mouse mandibular molar. The first method presented is based on a micro-CT setup enabling phase enhancement&#160;imaging of fresh collagen tissues. The second method is a bone clearing method using ethyl cinnamate that enables imaging through the bone without sectioning and preserves endogenous fluorescence. Combining this clearing method with reporter mice like Flk1-Cre;TdTomato provided a first of its kind opportunity to image the 3D vasculature in the PDL and alveolar bone.

We present a protocol for generating orthodontic tooth movement in mice and methods for 3D visualization of the collagen fibers and blood vessels of periodontal ligament without sectioning.

Orthodontic tooth movement is a complex biological process of altered soft and hard tissue remodeling as a result of external forces. In order to ...

A Standardized Approach to Extra-Oral and Intra-Oral Digital Photography

Contemporary dentistry mandates a more comprehensive and personalized analysis of each patient. Technological advances in digital photography have played vital roles in diagnostic accuracy, treatment planning, execution of therapies, and outcome evaluations, including esthetic enhancement. Digital photography also provides an excellent platform for patient education, communication, and co-management of cases with other healthcare providers. However, intra-oral photography often faces challenges such as inaccessibility of areas to be captured, different moveable vs. fixed tissues involved, contamination with saliva or blood, and differing illumination needs on various locations. Thus, a more standardized and systematic approach is proposed for intra- and extra-oral documentation via digital photography to overcome the existing technical challenges. The current work will outline the appropriate equipment specifications (camera bodies, macro lens, and flashes), positions and postures of the operator and patients, proper techniques of tissue retraction, the use of appropriate intra-oral mirrors, and the essential elements such as aperture settings (F-stop), ISO, shutter speed, and white balance. This article aims to provide all dental professionals with an approachable linear array of guidelines to produce simplified and standardized visual tools for more efficient and effective documentation.

The present protocol describes a method to capture intra- and extra-oral photographs for digital documentation in the clinical practice of dentistry. The protocol highlights all the critical components involved and the commonly utilized settings to capture dental photographs for clinical documentation.

Contemporary dentistry mandates a more comprehensive and personalized analysis of each patient. Technological advances in digital photography have played ...

A Murine Maxillary Orthodontic Model Guide to Bone Remodeling Analysis

The Establishment of a Murine Maxillary Orthodontic Model

Due to the lack of reproducible protocols for establishing a murine maxillary orthodontic model, we present a reliable and reproducible protocol to provide researchers with a feasible tool to analyze mechanical loading-associated bone remodeling. This study presents a detailed flowchart in addition to different types of schematic diagrams, operation photos, and videos. We performed this protocol on 11 adult wide-type C57/B6J mice and harvested samples on postoperative days 3, 8, and 14. The micro-CT and histopathological data have proven the success of tooth movements coupled with bone remodeling using this protocol. Furthermore, according to the micro-CT results on days 3, 8, and 14, we have divided bone modeling into three stages: preparation stage, bone resorption stage, and bone formation stage. These stages are expected to help researchers concerned with different stages to set sample collection time reasonably. This protocol can equip researchers with a tool to carry out regenerative analysis of bone remodeling.

Author Spotlight: Insights into an Efficient Murine Maxillary Orthodontic Model Protocol

Here we demonstrate step-by-step a manageable, orthodontic tooth movement protocol operated on a murine maxillary model. With the explicit explanation of each step and visual demonstration, researchers can master this model and apply it to their experimental needs with a few modifications.

Due to the lack of reproducible protocols for establishing a murine maxillary orthodontic model, we present a reliable and reproducible protocol to ...

Artificial Intelligence-Based Integration of Dental Images

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

This study aimed to introduce cone-beam computed tomography (CBCT) digitization and integration of digital dental images (DDI) based on artificial intelligence (AI)-based registration (ABR) and to evaluate the reliability and reproducibility using this method compared with those of surface-based registration (SBR). This retrospective study consisted of CBCT images and DDI of 17 patients who had undergone computer-aided bimaxillary orthognathic surgery. The digitization of CBCT images and their integration with DDI were repeated using an AI-based program. CBCT images and DDI were integrated using a point-to-point registration. In contrast, with the SBR method, the three landmarks were identified manually on the CBCT and DDI, which were integrated with the iterative closest points method.
After two repeated integrations of each method, the three-dimensional coordinate values of the first maxillary molars and central incisors and their differences were obtained. Intraclass coefficient (ICC) testing was performed to evaluate intra-observer reliability with each method&#39;s coordinates and compare their reliability between the ABR and SBR. The intra-observer reliability showed significant and almost perfect ICC in each method. There was no significance in the mean difference between the first and second registrations in each ABR and SBR and between both methods; however, their ranges were narrower with ABR than with the SBR method. This study shows that AI-based digitization and integration are reliable and reproducible.

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

A process of registering cone-beam computed tomography scans and digital dental images has been presented using artificial intelligence (AI) -assisted identification of landmarks and merging. A comparison with surface-based registration shows that AI-based digitization and integration are reliable and reproducible.

This study aimed to introduce cone-beam computed tomography (CBCT) digitization and integration of digital dental images (DDI) based on artificial ...

A Pilot Study Showing Application of Palatal Bone Harvesting Under Dynamic Navigation Guidance in Oral Implantology

In cases of insufficient bone volume in the implant area, bone augmentation surgery is often required to ensure an adequate amount of bone around the implant. In autologous bone grafting procedures, some patients face insufficient bone volume at conventional intraoral bone harvesting sites. Due to the high difficulty associated with completing palatal osteotomies freehand, fewer palatal lateral osteotomies have been reported, and palatal bone blocks are not routinely used in bone augmentation procedures. With the development of digitally guided implant technology, palatal bone harvesting has become feasible. In this paper, we will present a method of obtaining a circumferential bone block of the palate for implant surgical application through dynamic navigation guidance. A total of three patients had undergone this procedure to obtain circumferential palatal bone blocks and completed bone augmentation surgery. The bone augmentation results in the implanted area have been favorable, and there has been some bone recovery at the harvesting site. This is a safe and effective way to obtain intraoral bone blocks.

The protocol introduces a novel surgical method for palatal ring bone block harvesting using dynamic navigation guidance, addressing the challenge of insufficient bone volume in typical intraoral grafting areas and offering a viable solution for effective bone augmentation in implant dentistry.

In cases of insufficient bone volume in the implant area, bone augmentation surgery is often required to ensure an adequate amount of bone around the ...

Investigating Orthodontic Tooth Movement and Bone Adaptation Mechanisms in Mice

Studying Orthodontic Tooth Movement in Mice

Orthodontic tooth movement (OTM) represents a dynamic process in which the alveolar bone undergoes resorption at compression sites and deposition at tension sites, orchestrated by osteoclasts and osteoblasts, respectively. This mechanism serves as a valuable model for studying various aspects of bone adaptation, including root resorption and the cellular response to mechanical force stimuli. The protocol outlined here offers a straightforward approach to investigate OTM, establishing 0.35 N as the optimal force in a mouse model employing a nickel-titanium (NiTi) coil spring. Utilizing micro-computed tomography analysis, we quantified OTM by assessing the discrepancy in the linear distance at the cement-enamel junction. The evaluation also included an analysis of orthodontic-induced inflammatory root resorption, assessing parameters such as root mineral density and the percentage of root volume per total volume. This comprehensive protocol contributes to advancing our understanding of bone remodeling processes and enhancing the ability to develop effective orthodontic treatment strategies.

Here, we present a protocol for studying orthodontic tooth movement (OTM), serving as a suitable model for investigating the mechanisms of bone adaptation, root resorption, and the response of bone cells to mechanical stimuli. This comprehensive guide provides detailed information on the OTM model, micro-computed tomography acquisition, and subsequent analysis.

Orthodontic tooth movement (OTM) represents a dynamic process in which the alveolar bone undergoes resorption at compression sites and deposition at ...

Measuring Maxillary Posterior Tooth Movement: A Model Assessment using Palatal and Dental Superimposition

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