The Angle orthodontist最新文献

Objectives: To assess mandibular lingual bone thickness changes after molar distalization with microimplants and during retention.

Materials and methods: Twenty-one patients (10 men, 11 women; mean age: 20.5 ± 4.9 years) who underwent mandibular molar distalization with microimplants were included. Cone-beam computed tomography images at pretreatment (T0), posttreatment (T1), and retention (T2) were used to measure posterior space available and lingual bone thickness distal to the mandibular second molar at 0-, 2-, 4-, and 6-mm levels apical to the root furcation. Repeated measures analysis of variance with Bonferroni correction was applied to compare T0, T1, and T2 measurements. Pearson's correlation analysis assessed the relationship between lingual bone thickness change and other variables.

Results: The mandibular second molar moved distally by 3.0 mm at crown level, and 1.2-1.8 mm at root level, after treatment. Posterior space available decreased significantly with root-cortex contact or radiographic lingual bone dehiscence observed at 6-mm root level. After retention, reduced cortical bone thickness increased significantly; however, T2 lingual bone thickness was less than T0. Although the decrease in lingual bone thickness at 6-mm root level correlated with crown and root distal movement after treatment, the increase in bone thickness during retention was not associated with tooth movement, patient age, or retention duration.

Conclusions: Mandibular lingual bone thickness noticeably decreased after molar distalization with microimplants. After retention, significant bone recovery formation was observed at the thinned lingual cortex or radiographic bone dehiscence.

Objectives: To investigate transverse treatment outcomes in patients with skeletal Class III malocclusion treated with a surgery-first orthognathic approach (SFA) vs conventional orthognathic surgery (COS).

Materials and methods: This retrospective cohort study included 128 patients, divided into four groups of 32 based on the inclusion of presurgical treatment and extraction of the maxillary premolars: (1) COS with extraction, (2) COS without extraction, (3) SFA with extraction, and (4) SFA without extraction. CBCT scans were taken before and after treatment, with an additional scan after presurgical orthodontic treatment for the COS group only. The primary outcome variable was transverse decompensation, assessed through changes in maxillary and mandibular molar inclination and intermolar width. Predictor variables included treatment approach (SFA vs COS) and extraction status (extraction vs nonextraction). Transverse measurements were compared among the four groups throughout the treatment process.

Results: Maxillary molar inclination relative to the occlusal plane increased after treatment, whereas the mandibular molar inclination decreased after treatment, indicating transverse decompensation in the COS and SFA groups, and the extraction and nonextraction groups. There were no statistically significant differences in transverse changes between the COS and SFA groups.

Conclusions: Although the difference in transverse decompensation between the COS and SFA groups was not statistically significant, clinicians may still need to consider careful management of transverse decompensation during postsurgical treatment, particularly in SFA cases.

Objectives: To compare efficacy and treatment duration of early versus late Class II elastics in patients with Class II malocclusion.

Material and methods: Forty patients were randomized into two groups based on the timing of elastics use: early and late. In the early group, light short elastics were used from the day of placement of fixed preadjusted edgewise appliances. In the late group, elastics were inserted once 0.016 × 0.022-inch stainless steel archwires were in place. Lateral cephalograms and standardized smile photographs were taken before treatment and after achieving a Class I buccal segment relationship. Treatment duration, dental, skeletal, and soft tissue measurements were then compared between the two groups.

Results: Maxillary central incisors were retroclined relative to the SN plane (95% confidence interval (CI): 3.75°-11.99° and 3.96°-9.18° in the early and late groups, respectively) with clockwise rotation of the occlusal plane (95% CI: 3.75°-11.99° and 3.96°-9.18° in the early and late groups, respectively). Treatment duration to level and align and reach Class I buccal occlusion was significantly less in the early group (95% CI: 4.74-10.8 months). Comparison between groups revealed no significant differences for all measurements except MP/SN and PP/SN angles (P < .05).

Conclusions: Class II elastics were equally effective and more efficient in the early group with significantly less time needed to level and align and reach Class I buccal occlusion compared to the late group.

Objectives: To evaluate the accuracy of three fully automated software systems compared to nonautomated cephalometric analysis software tested using cephalograms featuring correct and incorrect head positions.

Materials and methods: The study sample consisted of 40 lateral cephalograms retrieved retrospectively from a larger pool of pretreatment orthodontic records. Cephalograms were recruited and divided into correct head posture group (CHP) and incorrect head posture group (IHP). Cephalometric data were obtained by manual landmarking (Dolphin software), which served as a reference, and by fully automated AI software (WebCeph, Ceph Assistant, and AudaxCeph). Intraclass correlation coefficients (ICC) and paired t-tests were used for intragroup comparisons, whereas analysis of variance and post-hoc analysis were used to compare performance among artificial intelligence (AI) based software applications.

Results: The tested software exhibited a good level of consistency for angular measurements whereas linear measurements were more error-prone. AudaxCeph demonstrated the most consistent accuracy, achieving excellent agreement (ICC > 0.90) for several skeletal parameters; however, it failed in detecting soft tissue accurately. WebCeph and Ceph Assistant showed greater variability, especially for linear measurements (ICC < 0.50). Positional errors drastically reduced measurement accuracy, with linear parameters such as Go-Me showing the poorest agreement across all software.

Conclusions: AI-based cephalometric software demonstrated variable accuracy depending on the cephalometric measurement, and this pattern was exacerbated under conditions involving positional errors in cephalograms. Accordingly, oversight by expert clinicians is still required to minimize marginal error.

Objectives: To evaluate and compare the validity and reliability of different artificial intelligence (AI) chatbots in answering queries about potential orthodontic risks.

Materials and methods: Answers to 20 frequently asked questions about the potential risks of orthodontics were derived from daily consultations with experienced orthodontists and AI chatbots (ChatGPT 4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro). The questions were repeated three times and submitted to the AI chatbots to assess the reliability of their answers. The answers from AI chatbots were scored using a modified Global Quality Scale (GQS). Low- and high-threshold validity tests were used to determine validity, and Cronbach's alpha was used to evaluate the consistency of the three responses to each of the 20 questions.

Results: In the low-threshold validity test, Gemini exhibited the highest overall performance. In the high-threshold validity test, Gemini also showed the highest overall effectiveness, but there was no significant difference observed among the three chatbots. All three chatbots demonstrated satisfactory levels of reliability, with Gemini having the highest consistency.

Conclusions: AI chatbots have some potential in providing orthodontic risk information, but they must be used cautiously and further optimized to improve their effectiveness in clinical practice.

Objectives: To suggest a sample size calculation method to develop artificial intelligence (AI) that can predict soft tissue changes after orthognathic surgery with clinically acceptable accuracy.

Materials and methods: From data collected from 705 patients who had undergone combined surgical-orthodontic treatment, 10 subsets of the data were generated through random resampling procedures, specifically with reduced data sizes of 75, 100, 150, 200, 300, 400, 450, 500, 600, and 700. Resampling was repeated four times, and each subset was used to create a total of 40 AI models using a deep-learning algorithm. The prediction results for soft tissue change after orthognathic surgery were compared across all 40 AI models based on their sample sizes. Clinically acceptable accuracy was set as a 1.5-mm prediction error. The predictive performance of AI models was evaluated on the lower lip, which was selected as a primary outcome variable and a benchmark landmark. Linear regression analysis was conducted to estimate the relationship between sample size and prediction error.

Results: The prediction error decreased with increasing sample size. A sample size greater than 1700 datasets was estimated as being required for the development of an AI model with a prediction error < 1.5 mm at the lower lip area.

Conclusions: A fairly large quantity of orthognathic surgery data seemed to be necessary to develop software programs for visualizing surgical treatment objectives with clinically acceptable accuracy.

Objectives: To evaluate the influence of lip protrusion and thickness on the perception of facial profile attractiveness among subjects with different ethnic backgrounds.

Materials and methods: 424 participants were divided into four groups (European, Black African, Far Eastern, and Middle Eastern) and further subdivided into two subgroups according to age (18 to 40 and 41 to 60 years). An idealized female profile silhouette image was manipulated to generate 18 images with three different lip thicknesses and six sagittal lip positions. To assess perception of facial profile attractiveness, participants completed the developed questionnaire.

Results: Statistically significant differences were found among subjects with different ethnic backgrounds for all images (P < .01). Percent agreement averaged 13.89%. Within each group, scores varied with lip thickness and protrusion, with significant interaction between the two factors. Gender and age had a significant impact on profile attractiveness mean scores.

Conclusions: Ricketts norms for the most favorable lip position to E-line need to be updated. Middle Eastern and Europeans regarded lips positioned + 1 mm to the norm in relation to E-line as the most attractive. Thick lips that were mildly protruded were preferred by Africans. Far Eastern participants preferred normal thickness and thin lips that were protrusive; thick lips were ranked lowest. Among the thick lips, protrusive lips were ranked higher. More personalized and culturally sensitive orthodontic treatment planning is needed to help patients achieve their desired facial esthetic outcome.

Objectives: To compare and validate two tridimensional diagnostic methods for quantifying and categorizing external root resorption using an artificial intelligence (AI)-aided, automatic, or manual digital segmentation process.

Materials and methods: 40 teeth were segmented from 10 cone beam computed tomography (CBCT) records from five patients. Stereolithographic files were created, and automatic, manual, or AI-aided segmentation of each incisor was performed by two double-blinded operators. Two quantification methods were used and compared by analyzing final segmented regions of the tooth. This study followed QAREL (Quality Appraisal of Diagnostic Reliability) and COSMIN (COnsensus-based Standards for the selection of health Measurement Instruments) guidelines. Reproducibility was assessed using the Dahlberg formula, coefficient of variation, and intraclass correlation coefficient (ICC) (P value < .05).

Results: Intra- and interobserver correlations were high (ICC: > 0.736; P < .01). Statistically significant differences were found between the two measurement methods for high-quality CBCT images of central incisors, mainly at the level of the apical third. Specific differences were found between methods when root resorption was evaluated in the middle and apical thirds using AI segmentation of the central incisor (P = .043). When referring to total volume loss of the lateral incisor, differences (P = .021) were observed between methods when segmented by manual or AI-aided procedures. Highest specificity (100%) was observed for AI-aided segmentation and Method 2 for evaluation of root resorption at the apical third volume.

Conclusions: Assessment of root resorption with CBCT is highly dependent on CBCT definition, type of segmentation, and measurement method. Three-dimensional (3D) measurement method described by three landmark points yielded satisfactory results using any tested segmentations.

Objectives: To examine how defects in alveolar bone affect movement of teeth during orthodontic treatment.

Materials and methods: Pretreatment cone-beam computed tomography images from 26 patients: 15 females and 11 males, with a mean age of 21.5 years (SD ± 3.7 years), were used to evaluate the buccal alveolar bone on the maxillary canine. Maxillary canines (n = 52) were subsequently categorized into three groups: control or no bone defects (n = 17), fenestration (n = 20), and quasidefect (n = 15). Each canine was displaced distally for 16 weeks using nickel-titanium closed coil springs (50 g) and segmental archwire mechanics. The rate and amount of tooth movement were evaluated using superimposition of lateral cephalograms and three-dimensional digital dental models between before and after canine retraction. Rate of tooth movement was evaluated among different bone defect groups.

Results: Rate of movement was significantly decreased in the fenestration (0.87 ± 0.23 mm/mo) and quasidefect groups (0.62 ± 0.14 mm/mo) compared to the control group (1.17 ± 0.40 mm/mo). Also, 85% of all subjects exhibited an evident asymmetric pattern of tooth movement, and 77% of these subjects presented with unilateral bone defects.

Conclusions: The type and existence of alveolar bone defects have a substantial effect on rate of tooth movement. Therefore, when conducting orthodontic tooth movement investigations and planning orthodontic treatment, it is important to consider the existence of alveolar bone defects.