Which factors influence orthodontists in their decision to extract? A questionnaire survey

Second Versus First Molar Extractions in Class II Division 1 Malocclusion Treatment: A Retrospective Longitudinal Outcome Study into Maxillary Canine, Premolar, and Molar Movement

Background/objectives: This retrospective longitudinal outcome study comparing orthodontic extraction modalities, including extraction of maxillary first or second molars, aimed to compare the three-dimensional tooth movement of maxillary canines (C), premolars (P1, P2), and molars (M1, M2) in Class II division 1 malocclusion treatment with fixed appliances. Methods: A sample of 98 patients (mean age 13.20 ± 1.46 years) was selected for the M1 group, and 64 patients (mean age 13.20 ± 1.36 years) were chosen for the M2 group. Tooth movement was analyzed three-dimensionally on pre-treatment (T0) and post-treatment (T1) digital dental casts. Regression analyses compared the tooth movements (in mm) between the M1 and M2 groups. Results: The mean treatment duration for the M1 group was 2.51 ± 0.55 year, while, for the M2 group, it was 1.53 ± 0.37 year. The data showed limited distal movements of the C, P1, and P2 of approximately 2 mm in the M1 group and 1 mm in the M2 group during orthodontic treatment, but the M1 group exhibited significantly more distal movements than the M2 group (mean difference 1.11 to 1.24 mm). Vertical movements of the C, P1, and P2 in both groups were also minor (0.16 to 1.26 mm). The differences between groups did not exceed 0.2 mm and were not significant. Both treatment modalities resulted in a significant degree of anchorage loss with a distinct mesialization (8.40 ± 1.66 mm) of M2 in the M1 group and limited distalization (0.83 ± 0.98 mm) of M1 in the M2 group. Conclusions: The findings highlight the importance of thorough case evaluation when choosing between extraction modalities in Class II treatment. If a large distal movement of canines and premolars is required, additional anchorage mechanics should be considered.

Evaluation of different machine learning algorithms for extraction decision in orthodontic treatment

INTRODUCTION

The extraction decision significantly affects the treatment process and outcome. Therefore, it is crucial to make this decision with a more objective and standardized method. The objectives of this study were (1) to identify the best-performing model among seven machine learning (ML) models, which will standardize the extraction decision and serve as a guide for inexperienced clinicians, and (2) to determine the important variables for the extraction decision.

METHODS

This study included 1000 patients who received orthodontic treatment with or without extraction (500 extraction and 500 non-extraction). The success criteria of the study were the decisions made by the four experienced orthodontists. Seven ML models were trained using 36 variables; including demographic information, cephalometric and model measurements. First, the extraction decision was performed, and then the extraction type was identified. Accuracy and area under the curve (AUC) of the receiver operating characteristics (ROC) curve were used to measure the success of ML models.

RESULTS

The Stacking Classifier model, which consists of Gradient Boosted Trees, Support Vector Machine, and Random Forest models, showed the highest performance in extraction decision with 91.2% AUC. The most important features determining extraction decision were maxillary and mandibular arch length discrepancy, Wits Appraisal, and ANS-Me length. Likewise, the Stacking Classifier showed the highest performance with 76.3% accuracy in extraction type decisions. The most important variables for the extraction type decision were mandibular arch length discrepancy, Class I molar relationship, cephalometric overbite, Wits Appraisal, and L1-NB distance.

CONCLUSION

The Stacking Classifier model exhibited the best performance for the extraction decision. While ML models showed a high performance in extraction decision, they could not able to achieve the same level of performance in extraction type decision.

Perception of facial esthetics and cephalometric correlations in Class II patients: a comparison between two-phase and one-phase treatments

An effective orthodontic treatment should not only aim for satisfactory occlusal outcomes but also consider its impact on facial esthetics. The study aims to evaluate and compare the perception of profile esthetics of skeletal Class II patients treated with two orthodontic modalities: (1) Two-phase approach involving functional appliances followed by fixed appliances with premolar extractions, or (2) One-phase approach using fixed appliances with premolar extractions. Additionally, the study aims to evaluate the correlation between the perceived esthetics and the corresponding cephalometric measurements. The study included 40 skeletal Class II adolescents who underwent either two-phase (n = 20, mean age = 12.38 ± 1.18) or one-phase (n = 20, mean age = 12.53 ± 0.79) orthodontic treatments. Eighty profile silhouettes (pre- and post-treatment) were assessed by 64 raters, including 23 orthodontists, 21 general dental practitioners, and 20 laypersons. The raters used a visual analog scale (VAS) to access profiles, upper and lower lips, and chin esthetics. At pre-treatment, all three groups of raters gave significantly lower scores to the profile silhouettes of the two-phase group compared to the one-phase group (P < 0.01); however, after treatment, they rated the two-phase group significantly higher (P ≤ 0.001). The two-phase group exhibited greater improvements in profile and upper and lower lip esthetics as perceived by all raters (P ≤ 0.001). Furthermore, cephalometric results revealed greater reductions in SNA, ANB, Wits appraisal, and G'-Sn-Pog' in the two-phase group compared to the one-phase group (P < 0.05). Five cephalometric parameters (SNB, SNPog, overjet, overbite, and UL-SnPog') demonstrated significant correlations with VAS scores given by orthodontists (P < 0.05). In conclusion, the two-phase group showed greater subjective and objective improvements in facial esthetics than the one-phase group. Additionally, the anteroposterior mandibular position and upper lip protrusion may be the primary cephalometric parameters correlated with subjective facial profile perceptions.

Effectiveness of Machine Learning in Predicting Orthodontic Tooth Extractions: A Multi-Institutional Study

The study aimed to evaluate the effectiveness of machine learning in predicting whether orthodontic patients would require extraction or non-extraction treatment using data from two university datasets. A total of 1135 patients, with 297 from University 1 and 838 from University 2, were included during consecutive enrollment periods. The study identified 20 inputs including 9 clinical features and 11 cephalometric measurements based on previous research. Random forest (RF) models were used to make predictions for both institutions. The performance of each model was assessed using sensitivity (SEN), specificity (SPE), accuracy (ACC), and feature ranking. The model trained on the combined data from two universities demonstrated the highest performance, achieving 50% sensitivity, 97% specificity, and 85% accuracy. When cross-predicting, where the University 1 (U1) model was applied to the University 2 (U2) data and vice versa, there was a slight decrease in performance metrics (ranging from 0% to 20%). Maxillary and mandibular crowding were identified as the most significant features influencing extraction decisions in both institutions. This study is among the first to utilize datasets from two United States institutions, marking progress toward developing an artificial intelligence model to support orthodontists in clinical practice.

AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning-A Comprehensive Review

The advent of artificial intelligence (AI) in medicine has transformed various medical specialties, including orthodontics. AI has shown promising results in enhancing the accuracy of diagnoses, treatment planning, and predicting treatment outcomes. Its usage in orthodontic practices worldwide has increased with the availability of various AI applications and tools. This review explores the principles of AI, its applications in orthodontics, and its implementation in clinical practice. A comprehensive literature review was conducted, focusing on AI applications in dental diagnostics, cephalometric evaluation, skeletal age determination, temporomandibular joint (TMJ) evaluation, decision making, and patient telemonitoring. Due to study heterogeneity, no meta-analysis was possible. AI has demonstrated high efficacy in all these areas, but variations in performance and the need for manual supervision suggest caution in clinical settings. The complexity and unpredictability of AI algorithms call for cautious implementation and regular manual validation. Continuous AI learning, proper governance, and addressing privacy and ethical concerns are crucial for successful integration into orthodontic practice.

Statistical Study Concerning Extraction and Non-Extraction Methods in Orthodontic Treatments

INTRODUCTION

The need to extract permanent teeth for the correction of dento-maxillary anomalies remains even today a controversy in orthodontic therapy. The purpose of this study was to assess the orthodontist's point of view on the extraction and non-extraction methods used in orthodontic treatments, regarding clinical experience.

MATERIAL AND METHODS

A de novo conceived questionnaire was elaborated by means of Google Forms and it comprised 22 questions regarding gender, age, different proprieties and aspects of both extraction and non-extraction methods and the percentage of treated patients who developed an increase in dental sensitivity and caries activity following the therapeutic stripping procedure.

RESULTS

204 orthodontists answered the questionnaire. The respondents chose dental stripping (IPR-interproximal reduction) as the most used non-extraction method in their orthodontic practice with 34,3%, followed by over-expansion and dental stripping 24%. The probability that females will try to avoid dental extractions is 1.888 times higher compared to males. The association is statistically significant (p-value 0.0336). 70,1% of clinicians state that they use dental stripping as a non-extraction method to gain space even when there is NO Bolton discrepancy. In Class III cases, for reducing crowding and correcting the dental relations, 65.2% of doctors opt for dental stripping, while 34.8% do not avoid the extraction of a lower incisor.

CONCLUSIONS

Among the non-extraction methods for creating space, the most used by the surveyed orthodontists was dental stripping IPR. Tooth extraction remains a solution used by orthodontists for treating dentomaxillary disharmonies with crowding.

Evaluation of the relationship between incisor torque and profile aesthetics in patients having orthodontic extractions compared to non-extractions

OBJECTIVE

The aim of the present study was to evaluate the relationship between soft tissues aesthetics and incisor torque, as well as the effect of crowding, anchorage, and extraction pattern, in adult patients treated without extraction or with two or four extractions.

MATERIALS AND METHODS

Seventy-seven subjects with permanent dentition were selected retrospectively. Among these, 24 patients were treated with four extractions, 24 with two extractions and 29 without extractions. Lateral cephalograms and photographies taken before (T0) and after (T1) treatment were retrieved. The amount of crowding and the type of anchorage were recorded, and a cephalometric analysis was performed. A one-way ANOVA was used to compare the variables within and between groups. Linear regressions were performed to evaluate the effect of different predictors on soft tissues variables at T1.

RESULTS

The statistical analysis showed no differences within and between groups for soft tissue aesthetics. A significant reduction of the angle obtained from the intersection of Frankfurt plane and mandibular plane was observed in the four-extractions group, and a significant proclination of the lower incisors was observed in the two-extraction group. Linear regressions revealed that the change in soft tissue profile aesthetics was affected by the type of anchorage and the two-extractions pattern.

CONCLUSIONS

Similar soft tissue aesthetics were observed after treatment in the three groups, despite the presence of some skeletal and dental differences.

CLINICAL RELEVANCE

A well-controlled incisor torque helps to preserve soft tissues aesthetics. The type of anchorage could influence soft tissues.

Evaluation of artificial intelligence model for crowding categorization and extraction diagnosis using intraoral photographs

Determining the severity of dental crowding and the necessity of tooth extraction for orthodontic treatment planning are time-consuming processes and there are no firm criteria. Thus, automated assistance would be useful to clinicians. This study aimed to construct and evaluate artificial intelligence (AI) systems to assist with such treatment planning. A total of 3,136 orthodontic occlusal photographs with annotations by two orthodontists were obtained. Four convolutional neural network (CNN) models, namely ResNet50, ResNet101, VGG16, and VGG19, were adopted for the AI process. Using the intraoral photographs as input, the crowding group and the necessity of tooth extraction were obtained. Arch length discrepancy analysis with AI-detected landmarks was used for crowding categorization. Various statistical and visual analyses were conducted to evaluate the performance. The maxillary and mandibular VGG19 models showed minimum mean errors of 0.84 mm and 1.06 mm for teeth landmark detection, respectively. Analysis of Cohen's weighted kappa coefficient indicated that crowding categorization performance was best in VGG19 (0.73), decreasing in the order of VGG16, ResNet101, and ResNet50. For tooth extraction, the maxillary VGG19 model showed the highest accuracy (0.922) and AUC (0.961). By utilizing deep learning with orthodontic photographs, dental crowding categorization and diagnosis of orthodontic extraction were successfully determined. This suggests that AI can assist clinicians in the diagnosis and decision making of treatment plans.

Determinants of Clinical Decision Making under Uncertainty in Dentistry: A Scoping Review

Clinical decision-making for diagnosing and treating oral and dental diseases consolidates multiple sources of complex information, yet individual clinical judgements are often made intuitively on limited heuristics to simplify decision making, which may lead to errors harmful to patients. This study aimed at systematically evaluating dental practitioners' clinical decision-making processes during diagnosis and treatment planning under uncertainty. A scoping review was chosen as the optimal study design due to the heterogeneity and complexity of the topic. Key terms and a search strategy were defined, and the articles published in the repository of the National Library of Medicine (MEDLINE/PubMed) were searched, selected, and analysed in accordance with PRISMA-ScR guidelines. Of the 478 studies returned, 64 relevant articles were included in the qualitative synthesis. Studies that were included were based in 27 countries, with the majority from the UK and USA. Articles were dated from 1991 to 2022, with all being observational studies except four, which were experimental studies. Six major recurring themes were identified: clinical factors, clinical experience, patient preferences and perceptions, heuristics and biases, artificial intelligence and informatics, and existing guidelines. These results suggest that inconsistency in treatment recommendations is a real possibility and despite great advancements in dental science, evidence-based practice is but one of a multitude of complex determinants driving clinical decision making in dentistry. In conclusion, clinical decisions, particularly those made individually by a dental practitioner, are potentially prone to sub-optimal treatment and poorer patient outcomes.

Evaluation of pharyngeal airway dimensions and cephalometric changes after premolar extraction and nonextraction orthodontic treatment in adolescent and adult patients

PURPOSE

The purpose of this study was to investigate the effects of premolar extraction and non-extraction for orthodontic treatment on the skeletal, dentoalveolar and pharyngeal airway.

MATERIAL AND METHOD

The study was carried out with the radiographs of a total of 130 patients at the ages of 14 to 25, including 59 male and 71 female patients. The cases were divided into two groups as the "extraction" group(2 upper premolar teeth) (n = 66) and the "non extraction" group (n = 64).

RESULTS

Extraction Group:In both age groups, there was a statistically significant increase in the mean interincisal angle (U1xL1) and lower anterior facial height (ANS-Me) values(p < 0.05). In the 14-18 age group, there was a significant increase in the mean upper airway thickness, inferior airway space, hyoid position, soft palate length(PNSP) and upper pharynx values(p < 0.05). In both sexes, while the mean U1xL1 and ANS-Me values significantly increased, the mean upper incisors inclination values decreased significantly(p < 0.05). Non Extraction Group:In both age groups, the mean ANS-Me values increased significantly from T0 to T1(p < 0.05). In the 14-18 age group, the increase in the mean upper adenoid thickness, tongue length,PNSP, softpalate thickness and vertical airway length values was significant(p < 0.05). In the male patients, the mean U1xL1 value decreased significantly(p < 0.05).

CONCLUSION

The premolar teeth extraction performed in the patients receiving orthodontic treatment did not affect airway dimensions to a high extent.It was determined that the position of the hyoid bone and tongue length was not affected in the patients with premolar extraction and non extraction.

A qualitative evaluation of attitudes toward extractions among primary care orthodontists in Great Britain

INTRODUCTION

The need to extract permanent teeth as part of orthodontic treatment has been keenly debated over many decades. Changes in the frequency of extraction have been well documented; however, we continue to lack an understanding of what influences clinicians' decisions regarding extracting permanent teeth.

METHODS

Purposive sampling was undertaken to obtain representative views from primary care practitioners across Great Britain with a range of experience representing genders and wide geographic distribution. Twenty participants (9 female, 11 male) took part in in-depth, qualitative, 1-to-1 interviews based on a piloted topic guide. Interviews were conducted via video conferencing software with audio recording and verbatim transcription. Thematic analysis was performed with discussion and agreement to identify the main themes.

RESULTS

Five main themes were identified: (1) patient-related factors, such as age and features of the malocclusion, (2) operator factors, including the level of experience, (3) setting, with regard to geographic location and method of remuneration, (4) mechanical approaches, including variations in appliance systems; and (5) self-directed ongoing education, including both formal continuing professional development and informal learning from peers. These factors acted as barriers, enablers, or both in relation to nonextraction treatment.

CONCLUSIONS

Five key influences on extraction decisions among orthodontists in Great Britain were identified. Extraction choices appear to be influenced by various interrelated factors, evolving over time and with increased experience.

Cephalometric and model evaluations after molar distalization using modified C-palatal plates in patients with severe arch length discrepancy

INTRODUCTION

This study aimed to evaluate the dentoskeletal and soft-tissue changes after molar distalization using modified C-palatal plates in patients with severe maxillomandibular arch length discrepancies.

METHODS

Twenty-four patients with Class I and II malocclusion (19.0 ± 7.3 years; 17 females and 8 males), who had severe maxillary crowding of >10 mm, and moderate mandibular crowding of >6 mm, underwent molar distalization using modified C-palatal plates and buccal miniscrews with approximately 300 g of force per side. Models were made, and cephalograms were taken before and after treatment. Cephalometric variables and arch dimensions were measured. Paired t test and Wilcoxon rank sum test were used for statistical analysis.

RESULTS

In the maxillary dentition, an average of 12.4 mm of crowding was resolved by molar distalization of 4.4 mm, interproximal stripping of 0.7 mm, and arch expansion. In the mandibular dentition, crowding of 6.7 mm was alleviated by molar distalization of 2.4 mm, an interproximal of 1.5 mm, and additional arch expansion. The incisor positions were maintained (SN-U1, 101.3°; IMPA, 88.8°), and soft-tissue profiles were improved (LL/E-line -1.1 mm; P < 0.001) after treatment.

CONCLUSIONS

Maxillary and mandibular tooth-size arch length discrepancy of 12.4 mm and 6.7 mm, respectively, were resolved by molar distalization, interproximal reduction, and arch expansion, whereas incisor positions were maintained, and soft-tissue profiles were improved. This could be a viable treatment option in patients with moderate-to-severe crowding.

Skeletal, Dental and Soft Tissue Cephalometric Changes after Orthodontic Treatment of Dental Class II Malocclusion with Maxillary First Molar or First Premolar Extractions

The aim of the present retrospective study was evaluating skeletal, dental and soft tissue changes of two groups of Class II patients orthodontically treated with extractions of upper first premolars (U4 group) and upper first molars (U6 group). In total, 21 patient records (9M and 12F; mean age 12.5 ± 1.2 years) were selected for the U4 group, and 38 patient records (17M and 21F; mean age 13.2 ± 1.3 years) were recruited for the U6 group. Twenty cephalometric variables were analysed on standardised lateral cephalograms at baseline (T0) and at the end of orthodontic treatment (T1). Means and standard deviations (SDs) were calculated for both groups and increments were calculated. After revealing the normal distribution of data with the Shapiro–Wilk test, Student’s t-test was used to compare variables at T0 between groups. A paired t-test was used to analyse changes between time points within each group, and Student’s t-test to compare differences between groups at T1. Both groups showed a significant increase in the distance among upper second molars and the vertical pterygoid line (PTV-maxillary second molar centroid U6 group: 6.66 ± 5.00 mm; U4 group: 3.66 ± 2.20 mm). Moreover, the distance of upper incisors to the palatal plane significantly increased (PP-maxillary incisor tip U6 group: 1.09 ± 1.52 mm; U4 group: 0.20 ± 2.00 mm; p = 0.061). Significant changes were found for overjet (U6 group: −4.86 ± 1.62 mm; U4 group: −3.27 ± 1.90 mm; p = 0.001). The distance between upper lip and esthetic plane showed a significantly reduction in both groups (ULip-E Plane U6 group: −2.98 ± 1.65 mm; U4 group: −1.93 ± 1.57 mm). No statistically significant changes were found in sagittal or vertical skeletal values. The significantly larger reduction of upper lip protrusion and overjet in the U6 group compared to the U4 group suggests preferring molar extraction treatment for severe Class II with protrusive soft tissues’ profile and increased overjet. Since no differences on vertical values were found, an increased SN^GoGn angle should not be considered a discriminating factor for choosing molar extraction treatment.

Soft tissue profile changes during treatment of patients with Class II malocclusion

Introduction/Objective. The class II malocclusion results in disbalanced facial harmony, primarily noticeable in the profile and the lower facial third. Aside from skeletal evaluation, orthodontic diagnosis and treatment planning should include facial soft tissue analysis. The aim of the study was to identify the soft tissue profile outcomes of orthodontic treatment of Class II, division 1 malocclusion patients and to determine if these changes are related with the different treatment protocol. Methods. The first group was the non-extraction group (25 patients) treated first with the Herbst appliance, and the second group was four premolars extraction group (25 patients) treated with a multibracket appliance. The patients? cephalograms and pre- and post-treatment profile photographs were used. Results. The improvement in the non-extraction group was evident in the decrease of the nasomental angle, the angle representing the projection of the upper lip to the chin, as well as the upper lip angle. In the extraction group, the nasolabial angle showed a significant increase. Soft tissue variables showed significant differences between the groups: the total facial angle or facial convexity including the nose and the angle presenting the projection of the upper lip to chin. Conclusion. The patients treated without extractions showed a significant improvement of the convex profile and favorable soft tissue changes in the lower third of the face.

Cephalometric predictors for optimal soft tissue profile outcome in adult Asian class I subjects treated via extraction and non-extraction. A retrospective study

OBJECTIVE

The aim of this retrospective study was to identify cephalometric predictors associated with favourable soft tissue profile outcomes after premolars extraction and non-extraction in class I malocclusion subjects.

MATERIALS AND METHODS

A total of 80 subjects, treated with non-extraction and premolars extraction (40 subjects each), were equally divided into favourable (FG) and unfavourable (UFG) groups using subjective and objective soft tissue profile outcome assessment methods. An independent t-test was utilized for the comparison of cephalometric measurements between the non-extraction (NE) and premolars extraction (PME) treatment modalities. Cox proportional hazard algorithm regression analysis was performed to identify cephalometric factors associated with favourable soft tissue outcomes.

RESULTS

The pre-treatment mean age of the NE group was 20.2±2.3 and PME group was 20.2±2.5 years. After dividing the sample of the NE and PME groups according to subjective and objective soft-tissue outcome assessment criteria, FG and UFG consisted of 20 subjects each. Cox proportional hazard algorithm regression analysis found upper incisor to NA angle (95% CI: 1.033, 1.196) to be associated with FG in NE and upper incisor to SN (95% CI: 1.018, 1.206) and ANB angle (95% CI:1.165, 3.608) in PME. There was a statistically significant strong correlation between subjective and objective evaluation methods (P≤0.001).

CONCLUSIONS

Cephalometric analysis is a valuable tool to predict soft-tissue outcomes after NE and PME. Increased upper incisors inclinations at the start of NE treatment result in favourable soft tissue profile outcomes. Slightly convex profile and proclined maxillary incisors are the predictors of favourable soft tissue profile outcome after PME. There was a statistically significant association between subjective and objective evaluation criteria of soft tissue outcomes.

Comparison of treatment effects from total arch distalization using modified C-palatal plates versus maxillary premolar extraction in Class II patients with severe overjet

INTRODUCTION

This study aimed to compare the skeletodental and soft tissue changes with total arch distalization using a modified C-palatal plate (MCPP) and maxillary first premolar extraction treatment in Class II malocclusion patients with severe overjet.

SETTING AND SAMPLE POPULATION

The sample consisted of 46 adult patients who had Class II Division 1 malocclusion with severe overjet; 25 of them received non-extraction treatment with MCPPs (age, 22.5 ± 7.2 years), and 21 received maxillary first premolar extraction treatment (age 23.4 ± 6.5 years).

METHOD

A total of 26 variables were measured on pre- and post-treatment lateral cephalograms. To evaluate the differences between pretreatment and post-treatment in each group, t tests and Wilcoxon rank-sum tests were used. To compare the amount of change between the two groups, MANOVA test was used.

RESULTS

The overjet was significantly reduced in the MCPP and extraction groups by 4.8 mm and 5.4 mm, respectively. However, the two groups had no significant difference in the sagittal, vertical and angular changes of the maxillary incisors. In addition, regarding soft tissue changes, the MCPP and extraction groups showed an increased nasolabial angle of 7.5° and 9.4°, decreased upper lip to the true vertical line of 1.8 mm and 2.2 mm, respectively (P < .001).

CONCLUSIONS

There was no significant difference in the skeletal changes between the MCPP and extraction groups, and the reduction in overjet was similar in the groups. These results suggest that MCPP might be a viable treatment option for total arch distalization in Class II malocclusion patients with severe overjet.

Three-dimensional soft tissue changes in orthodontic extraction and non-extraction patients: A prospective study

OBJECTIVES

To assess the soft tissue changes in orthodontic extraction and non-extraction patients on 3D stereophotogrammetric images.

SETTING AND SAMPLE

23 extraction (22.2 ± 9.2 years) and 23 non-extraction (20.3 ± 11.1 years) consecutive patients were enrolled at the Sections of Orthodontics at Aarhus University and at University of Naples Federico II.

METHODS

All patients had a first 3D image taken after bonding of brackets on the upper incisors (T0), and a second 3D image (T1) after space closure in the extraction group or at insertion of the first SS or TMA rectangular wire in the non-extraction group. The 3D images were captured with 3dMDFace System and analysed with 3dMDVultus Software. After placing 19 landmarks, 15 measurements were obtained. Intragroup changes were analysed with paired t-test and intergroup changes with unpaired t-test (P < .05).

RESULTS

Superimpositions of the 3D images at T0 and T1 visualized with colour-coded maps showed that soft tissue changes primarily happened in the perioral area in both groups. The Nasolabial angle increased significantly in the extraction group (3°± 4.1, P = .002), while it decreased in the non-extraction group (-1.5°± 5.5°, P = .002). There was a significant difference between the two groups (4.4°, P = .004).

CONCLUSIONS

3D comparison of the soft tissues in the extraction and non-extraction groups showed statistically significant, but clinically limited differences in the perioral area. The Nasolabial angle was significantly larger at T1 in the extraction group compared with the non-extraction group.

Machine learning from clinical data sets of a contemporary decision for orthodontic tooth extraction

OBJECTIVE

To examine the robustness of the published machine learning models in the prediction of extraction vs non-extraction for a diverse US sample population seen by multiple providers.

SETTING AND SAMPLE POPULATION

Diverse group of 838 patients (208 extraction, 630 non-extraction) were consecutively enrolled.

MATERIALS AND METHODS

Two sets of input features (117 and 22) including clinical and cephalometric variables were identified based on previous studies. Random forest (RF) and multilayer perception (MLP) models were trained using these feature sets on the sample population and evaluated using measures including accuracy (ACC) and balanced accuracy (BA). A technique to identify incongruent data was used to explore underlying characteristics of the data set and split all samples into 2 groups (G1 and G2) for further model training.

RESULTS

Performance of the models (75%-79% ACC and 72%-76% BA) on the total sample population was lower than in previous research. Models were retrained and evaluated using G1 and G2 separately, and individual group MLP models yielded improved accuracy for G1 (96% ACC and 94% BA) and G2 (88% ACC and 85% BA). RF feature ranking showed differences between top features for G1 (maxillary crowding, mandibular crowding and L1-NB) and G2 (age, mandibular crowding and lower lip to E-plane).

CONCLUSIONS

An incongruent data pattern exists in a consecutively enrolled patient population. Future work with incongruent data segregation and advanced artificial intelligence algorithms is needed to improve the generalization ability to make it ready to support clinical decision-making.

Surface conditioning of additively manufactured titanium implants and its influence on materials properties and in vitro biocompatibility

Customized osteosynthesis materials of titanium alloy can be generated by additive manufacturing replacing the complex adaptation to the patient individual anatomy, especially to the lower jaw bone which shows a highly individual surface area. After printing further conditioning is necessary to adjust surface roughness. The aim of the present study was to analyse the effect of different grinding and polishing procedures on sample surface and composition and in vitro biocompatibility. Ti-6Al-4V ELI samples printed by laser powder bed fusion (LPBF) were post-treated by multi-level procedures to adjust surface roughness using the surface conditioning technologies sandblasting, vibratory finishing, electro polishing or plasma polishing. Topography and chemical composition of the surfaces was analysed. Furthermore, the release of metal ions in contact to cell culture medium was quantified. Human osteoblasts as well as primary human gingiva cells (fibroblasts and epithelial cells) were cultivated in extracts or directly on the surfaces to analyse cytotoxicity, cell adhesion and cell proliferation. Surface roughness of the different materials after final polishing was in between 0.2 and 0.5 μm, which is in the same range as usually found for conventional titanium materials used in maxillofacial surgery. Furthermore, the wettability was comparable for all post-processing techniques. The chemical compositions of the finished surfaces showed a remarkable impact by the applied finishing technique. Extracts of the samples showed low cytotoxicity with exception of the plasma polished samples, which were shown to release significantly higher amounts of vanadium ions. Accordingly, cells showed good adhesion and proliferation on all samples except plasma polished specimens. Customized devices for midline osseodistraction were exemplarily printed with LPBF starting with patient's 3D data. Those devices can be considered for clinical use, since the printed and finished material meets the requirements of ISO 10993-5 for medical devices.