Accuracy and reliability of tooth widths and Bolton ratios measured by ClinCheck Pro

Predictability of mesiodistal tip of maxillary central incisors in clear aligner therapy

INTRODUCTION

Correct positioning of the maxillary central incisors (MxCIs) is a vital component of smile esthetics and the success of orthodontic treatment. This study investigated the efficacy of Invisalign SmartTrack aligners in achieving the mesiodistal tip of MxCIs, with an objective comparison to the ClinCheck (Align Technology, San Jose, Calif) prediction.

METHODS

A total of 174 MxCI teeth from 87 adult patients were selected from the Australasian Aligner Research Database. All patients exhibited pretreatment overlapping MxCI crowns and were treated with Invisalign SmartTrack aligners. The pretreatment tooth position, the planned mesiodistal tip movement using the ClinCheck software, and the achieved posttreatment tooth position were analyzed.

RESULTS

In the planned mesial root tip MxCI group, the mean planned and achieved mesial tips were 2.53° (standard error [SE], 0.29°) and 2.22° (SE, 0.42°), respectively, indicating an undercorrection of 0.31° (SE, 0.38°) (P >0.05). For the planned distal root tip MxCI group, the mean planned and achieved distal tips were 2.09° (SE, 0.22) and 0.39° (SE, 0.36°), respectively, indicating a mean undercorrection of 1.70° (SE, 0.39°) (P <0.001). There was no statistically significant association between the presence of attachments and the efficacy of the mesiodistal tip for MxCIs (P >0.05). No significant relationship was found (P >0.05) between the 1- or 2-week wear schedule and the efficacy of achieving the mesiodistal tip for MxCIs.

CONCLUSIONS

The overall percentage accuracy of mesiodistal tip movements for MxCIs when using Invisalign SmartTrack aligners was 53.3%. The presence of attachments and the influence of a 1- vs 2-week wear schedule were not found to have any significant influence on the efficacy of achieving planned mesiodistal tip movements for MxCIs.

Unveiling the role of artificial intelligence applied to clear aligner therapy: A scoping review

OBJECTIVES

To conduct a scoping review on the application of artificial intelligence (AI) in clear aligner therapy and to assess the extent of AI integration and automation in orthodontic software currently available to orthodontists.

DATA AND SOURCES

A systematic electronic literature search was performed in the following databases: PubMed, Embase, Web of Science, Cochrane Library, and Scopus. Also, grey literature resources up to March 2024 were reviewed. English-language studies on potential AI applications for clear aligner therapy were included based on an independent evaluation by two reviewers. An assessment of the automation steps in orthodontic software available on the market up to March 2024 was also conducted.

STUDY SELECTION AND RESULTS

Out of 708 studies, 41 were included. Sixteen articles focused on tooth segmentation, four on registration of digital models, 13 on digital setup, and eight on remote monitoring. Moreover, 13 aligner software programs were identified and assessed for their level of automation. Only one software demonstrated complete automation of the steps involved in the orthodontic digital workflow.

CONCLUSIONS

None of the 13 identified aligner software programs were evaluated in the 41 included studies. However, AI-based tooth segmentation achieved 98 % accuracy, while AI effectively merged CBCT and IOS data, supported digital measurements, predicted treatment outcomes, and showed potential for remote monitoring.

CLINICAL SIGNIFICANCE

AI applications in clear aligner therapy are on the rise. This scoping review enables orthodontists to identify AI-based solutions in orthodontic planning and understand its implications, which can potentially enhance treatment efficiency, accuracy, and predictability.

Diagnostic performance of ClinCheck, Dolphin Imaging, and 3D Slicer software for Bolton discrepancy analysis

OBJECTIVES

To evaluate the diagnostic performance of ClinCheck, Dolphin Imaging orthodontic software, and 3D Slicer for the analysis of Bolton discrepancy (BD).

MATERIALS AND METHODS

Fifty-five pairs of early-stage digital models of patients treated with Invisalign were printed to measure the BD by manual method with a digital caliper (gold standard). The discrepancy values calculated by ClinCheck were obtained. In addition, the sample STL files were measured using Dolphin Imaging and 3D Slicer software to obtain BD values. To assess reliability, precision, and accuracy of the methods, intraclass correlation coefficients (ICCs), Dahlberg's formula, paired t-tests, and the Bland-Altman method were used, respectively. Repeated-measures analysis of variance with Bonferroni post hoc test was used to assess the difference between groups.

RESULTS

The three methods showed reliable measurements (ICC ≥ 0.7), with the values of anterior Bolton slightly higher than overall Bolton. Measurements for the anterior Bolton showed higher precision (Dahlberg's formula 0.65, 0.70, and 0.55) than those for the overall Bolton. For anterior Bolton, only the measurements obtained by ClinCheck and Dolphin Imaging were accurate (P > .05, no proportion bias), while for overall Bolton, all groups had a significant difference. The Bland-Altman plots demonstrated no consistency for anterior Bolton measurements when 3D Slicer was used and for the overall Bolton.

CONCLUSIONS

ClinCheck and Dolphin Imaging showed accuracy to quantify anterior BD. For the overall Bolton measurements, ClinCheck showed a statistical difference from the manual assessment but without relevant clinical significance.

Reproducibility and reliability of dental arch measurements: comparing of manual, digital, and app-based methods

BACKGROUND

Knowing the available dental space in a patient is crucial for orthodontists to develop a good treatment plan.

OBJECTIVES

To compare and evaluate the reliability, reproducibility, and accuracy of three measurement methods on models: conventional, mobile app, and digital software.

MATERIALS AND METHODS

Maxillary and mandibular dental plaster models of 20 subjects with permanent dentition were analyzed. Three measurements were taken: manually using a digital caliper, using Nemocast 3D® software, and using the mobile app "Ruler." These were performed by the same observer at the same initial time (T1) and repeated after 15 days (T2).

RESULTS

The three methods measured each of the quadrants and each of the dental pieces similarly, with no significant differences found. Significant differences were found between the different measurement times in the manual and app ruler measurement groups. Intra-observer consistency was also observed through Pearson correlations. The measurement from the mobile app "Ruler" presented lower values compared to the other two models, especially with the measurements taken for each quadrant.

CONCLUSION

The three methods used for measuring dentoskeletal discrepancy are similar in terms of results. However, Nemocast 3D software showed greater reproducibility and reliability compared to the other two methods presenting consistency in the results measured at both time points, which may be useful for our diagnoses.

TRIAL REGISTRATION

The study was conducted according to the ethical standards set by the Declaration of Helsinki for Biomedical Research and the protocol was approved by the Bioethics Committee of the University of Salamanca with number 23/1074.

DilatedToothSegNet: Tooth Segmentation Network on 3D Dental Meshes Through Increasing Receptive Vision

The utilization of advanced intraoral scanners to acquire 3D dental models has gained significant popularity in the fields of dentistry and orthodontics. Accurate segmentation and labeling of teeth on digitized 3D dental surface models are crucial for computer-aided treatment planning. At the same time, manual labeling of these models is a time-consuming task. Recent advances in geometric deep learning have demonstrated remarkable efficiency in surface segmentation when applied to raw 3D models. However, segmentation of the dental surface remains challenging due to the atypical and diverse appearance of the patients' teeth. Numerous deep learning methods have been proposed to automate dental surface segmentation. Nevertheless, they still show limitations, particularly in cases where teeth are missing or severely misaligned. To overcome these challenges, we introduce a network operator called dilated edge convolution, which enhances the network's ability to learn additional, more distant features by expanding its receptive field. This leads to improved segmentation results, particularly in complex and challenging cases. To validate the effectiveness of our proposed method, we performed extensive evaluations on the recently published benchmark data set for dental model segmentation Teeth3DS. We compared our approach with several other state-of-the-art methods using a quantitative and qualitative analysis. Through these evaluations, we demonstrate the superiority of our proposed method, showcasing its ability to outperform existing approaches in dental surface segmentation.

Interproximal reduction in the refinement phase of Invisalign treatment: A quantitative analysis

INTRODUCTION

Interproximal reduction (IPR) is a common adjunct to contemporary orthodontic treatment. This study aimed to carry out a quantitative analysis of IPR prescribed in the refinement phases of clear aligner therapy with the Invisalign appliance (Align Technology, San Jose, Calif).

METHODS

The digital treatment plans (DTPs) of a total of 330 patients treated by 11 orthodontists were evaluated. Relevant data regarding patient age, gender, and prescription of IPR in the initial and refined DTPs were obtained from Align Technology's digital interface, ClinCheck. Computational analyses included descriptive statistics, Mann-Whitney U, and Kruskal-Wallis tests.

RESULTS

Most (n = 182; 75.2%) of the 242 patients who satisfied inclusion criteria were females. The median (interquartile range [IQR]) age was 29.2 (22.1-40.2) years. More than 60% of the contact sites prescribed IPR related to the initial DTP (n = 1312; 60.4%), with 39.6% (n = 859) recorded in the refinement DTPs. A median (IQR) of 1.1 (0.6-2.1) mm of IPR was prescribed per patient in the initial DTP compared with a median (IQR) of 0.6 (0.3-1.3) mm in the refinement DTPs. The most common site for prescribed IPR in all DTPs was the mandibular anterior region. Almost half (n = 108; 44.6%) of the patients were prescribed IPR at the same contact point site more than once during treatment.

CONCLUSIONS

Almost 40% of the contact points that were prescribed IPR were in the patients' refinement DTPs. Most IPR was prescribed for the anterior region of the mandible. Almost half of the patients had IPR repeatedly prescribed at the same sites during treatment.

Comparison of labio-palatal incisor movement between two wear protocols: a retrospective cohort study

OBJECTIVES

To investigate the accuracy of the Invisalign appliance in achieving predicted angular tooth movement of the maxillary central incisors, to locate the center of rotation in a labio-palatal direction, and to investigate any difference between 1-weekly and 2-weekly wear protocols.

MATERIALS AND METHODS

This study involved a retrospective sample of two groups of 46 Class I adult subjects treated non-extraction with different protocols of 1-weekly and 2-weekly wear. The pretreatment, predicted outcome and achieved outcome digital models were superimposed and measured using metrology software. Angular and center-of-rotation measurements in the sagittal plane for the maxillary right central incisor were analyzed.

RESULTS

There was a statistically significant difference between predicted and achieved angular measurements (P < .005) for labial tooth movements regardless of wear protocol. For palatal movements, no statistically significant difference was observed (P > .05). A small amount of overexpression was observed in some cases. Regarding crown and root control, uncontrolled tipping was the most predictable. No statistically significant difference was found between predicted and achieved center of rotation, but the confidence interval was wide. No statistically significant difference (P > .05) was found between the two wear protocols for the parameters measured.

CONCLUSIONS

For maxillary central incisors, labial angular movements were not as accurate as palatal movements. Overcorrection could be recommended with careful clinical monitoring due to the possibility of overexpression. Control of root movements may be unpredictable, and further research is required to draw stronger conclusions. For the parameters measured in this clinical sample, there was no difference between the two wear protocols.

Predicted overbite and overjet changes with the Invisalign appliance: a validation study

OBJECTIVES

To investigate whether the predicted changes in overbite (OB) and overjet (OJ) in Align Technology's (Santa Jose, Calif) digital treatment plan for the prescribed course of treatment with the Invisalign appliance were consistent with those same changes in OB and OJ measured with the Geomagic Control X (Geomagic US, Research, Triangle Park, NC) metrology software system.

MATERIALS AND METHODS

Geomagic Control X software was used to determine OB and OJ differences between the pretreatment and predicted outcome. STL files for patients satisfying inclusion criteria and undergoing nonextraction orthodontic treatment with the Invisalign appliance. The differences were compared, using the Bland-Altman analysis, to the corresponding data provided by Align Technology's digital treatment-planning interface, ClinCheck.

RESULTS

Data regarding 76 adult patients who satisfied strict inclusion criteria were assessed. The Shapiro-Wilks test indicated normality (P > .05). The Bland-Altman analysis showed high levels of agreement between the two measurements, with a bias range of -0.131 to 0.111 for OB and -0.393 to 0.03 for OJ recorded.

CONCLUSIONS

Clinicians and researchers can be confident that measurement data provided by Invisalign's ClinCheck digital treatment-planning facility concerning OB and OJ changes from the initial to the predicted treatment outcome are valid.

What is the current state of artificial intelligence applications in dentistry and orthodontics?

BACKGROUND

The use of Artificial Intelligence (AI) in the medical field has the potential to bring about significant improvements in patient care and outcomes. AI is being used in dentistry and more specifically in orthodontics through the development of diagnostic imaging tools, the development of treatment planning tools, and the development of robotic surgery. The aim of this study is to present the latest emerging AI softwares and applications in dental field to benefit from.

TYPES OF STUDIES REVIEWED

Search strategies were conducted in three electronic databases, with no date limits in the following databases up to April 30, 2023: MEDLINE, PUBMED, and GOOGLE® SCHOLAR for articles related to AI in dentistry & orthodontics. No inclusion and exclusion criteria were used for the selection of the articles. Most of the articles included (n = 79) are reviews of the literature, retro/prospective studies, systematic reviews and meta-analyses, and observational studies.

RESULTS

The use of AI in dentistry and orthodontics is a rapidly growing area of research and development, with the potential to revolutionize the field and bring about significant improvements in patient care and outcomes; this can save clinicians' chair-time and push for more individualized treatment plans. Results from the various studies reported in this review are suggestive that the accuracy of AI-based systems is quite promising and reliable.

PRACTICAL IMPLICATIONS

AI application in the healthcare field has proven to be efficient and helpful for the dentist to be more precise in diagnosis and clinical decision-making. These systems can simplify the tasks and provide results in quick time which can save dentists time and help them perform their duties more efficiently. These systems can be of greater aid and can be used as auxiliary support for dentists with lesser experience.

The predictability of the mandibular curve of Wilson, buccolingual crown inclination, and transverse expansion expression with Invisalign treatment

INTRODUCTION

This study aimed to provide an initial investigation into the mandibular curve of Wilson (COW) handling using the Invisalign appliance (Align Technology, Santa Clara, Calif). Individual buccolingual crown inclinations and transverse expansions were also investigated to detect any regions of ineffectiveness in the dental arch.

METHODS

A retrospective sample of patients treated by an experienced Invisalign provider in private practice was used. All adult patients with Class I or II Angle malocclusions and mild to moderate crowding treated using a minimum of 14 Invisalign aligners without intermaxillary elastics, bite ramps, or auxiliaries and a nonextraction mandibular arch from 2013-2019 were selected. Dental models at the initial scan, the ClinCheck prediction, and the outcome after the initial series of aligners were analyzed with Geomagic Control X software (version 2017.0.3; 3D systems, Rock Hill, SC). The COW, buccolingual crown inclination relative to the occlusal plane and cusp tip expansion were compared between ClinCheck changes and achieved changes.

RESULTS

Forty-two subjects met the selection criteria. 74% of subjects experienced a flatter COW than ClinCheck predictions, and the mean difference was 0.76 mm (P = 0.0149). The first molars encountered 0.52 mm (P <0.001) more buccal crown inclination than ClinCheck predictions. No other teeth experienced statistically significant buccolingual inclination differences to ClinCheck. The second molars were the only teeth to experience significantly more arch expansion than ClinCheck at 0.68 mm (P = 0.0046).

CONCLUSIONS

The Invisalign appliance tended to over-flatten the mandibular COW compared with ClinCheck because of the lack of buccal root torque in the mandibular first molars during expansion. The mandibular second molars were the only teeth to experience more expansion than ClinCheck.

Quantitative assessment of interproximal tooth reduction performed as part of Invisalign® treatment in 10 orthodontic practices

Background

Interproximal reduction (IPR) is a treatment option for orthodontic space gain. The attainment of prescribed objectives in aligner treatment may require IPR that is accurately performed both qualitatively and quantitatively.

Objective

This study assesses the in vivo accuracy of IPR carried out in 10 orthodontic practices as a method of orthodontic space creation.

Methods

A comparison of proposed and achieved amounts of IPR completed (accuracy), the accuracy of IPR within and between upper and lower dental arches, and the accuracy of IPR within and between posterior and anterior arch segments were performed using 3-dimensional digital study models gained via Align’s® ClinCheck.

Results

The findings indicated that IPR was routinely underperformed by all practices studied. On average, the amount of IPR achieved represented only 44.0% of the total prescribed per tooth in the sample assessed, with a mean discrepancy of 0.16 mm per tooth. There were statistically significant differences only between the overall anterior and posterior groups (p < 0.01) and between maxillary anterior and maxillary posterior groups (p < 0.01); however, these were not clinically significant. Significant differences in IPR performance were noted between different orthodontic practices.

Conclusions

This study demonstrates that the clinical performance of IPR in 10 orthodontic practices consistently fails to achieve the prescribed amount often by large variations. The effect of this under-performance on clinical outcomes remains to be quantified.