Effect of applying intermittent force with and without vibration on orthodontic tooth movement.

PURPOSE: This study aimed to determine whether orthodontic tooth movement could be accelerated by applying an intermittent force protocol. It also examined the effect of applying additional vibrational forces on orthodontic tooth movement and root resorption rates. METHODS: This study included 24 patients (16 males and 8 females) who underwent orthodontic treatment involving first premolar extraction and distal movement of the canines in the maxilla. A Hycon device (Adenta GmbH, Gilching, Germany) was used for canine distalization in all patients. The patients were randomly divided into two groups: one group received 20 min of vibration per day using the AcceleDent device (OrthoAccel Technologies, Inc., Bellaire, TX, USA), while the other group received no vibration. In addition, a split-mouth design was used: an activation-only force protocol was applied on one side, and an intermittent activation-deactivation-activation (ADA) protocol was applied on the other. The duration required for complete canine tooth distalization on each side was calculated. In addition, the effect of vibration on the orthodontically induced root resorption was examined. RESULTS: The intermittent ADA protocol significantly accelerated orthodontic tooth movement compared to the activation-only protocol (p < 0.05). The application of additional vibration did not affect the orthodontic tooth movement rate (p > 0.05). CONCLUSIONS: Using a Hycon device and following an ADA protocol provided significantly faster canine distalization than the activation-only protocol (p < 0.05). This intermittent force method proved very effective in closing the spaces. However, vibration did not significantly affect the orthodontic tooth movement rate (p > 0.05).

Comparison between cephalometric measurements using digital manual and web-based artificial intelligence cephalometric tracing software.

OBJECTIVE: The aim of this study was to compare the measurements performed with digital manual (DM) cephalometric analysis and automatic cephalometric analysis obtained from an online artificial intelligence (AI) platform, according to different sagittal skeletal malocclusions. METHODS: Cephalometric radiographs of 105 randomly selected individuals (mean age: 17.25 ± 1.87 years) were included in this study. Dolphin Imaging software was used for DM cephalometric analysis, and the WebCeph platform was used for AI-based cephalometric analysis. In total, 10 linear and 12 angular measurements were evaluated. The paired t-test, one-way ANOVA test, and intraclass correlation coefficient tests were used to evaluate the differences between the two methods. The level of statistical significance was set at p< 0.05. RESULTS: Except for SNB, NPog, U1.SN, U1.NA, L1-APog, I/I, and LLE parameters, all other parameters presented significant differences between the two methods (p< 0.05). While there was no difference (p> 0.05) in both SNA and SNB measurements between the two methods in the Class I malocclusion group, there was a difference between both methods in the Class II malocclusion group. Meanwhile, only the SNA in the Class III malocclusion group was different (p< 0.05). The ANB angle differed significantly in all three malocclusion groups. For both methods, all parameters except CoA and CoGn were found to have good correlation. CONCLUSION: Although significant differences were detected in some measurements between the two cephalometric analysis methods, not all differences were clinically significant. The AI-based cephalometric analysis method needs to be developed for more specific malocclusions.

Comparison between cephalometric measurements using digital manual and web-based artificial intelligence cephalometric tracing software

ABSTRACT Objective: The aim of this study was to compare the measurements performed with digital manual (DM) cephalometric analysis and automatic cephalometric analysis obtained from an online artificial intelligence (AI) platform, according to different sagittal skeletal malocclusions. Methods: Cephalometric radiographs of 105 randomly selected individuals (mean age: 17.25 ± 1.87 years) were included in this study. Dolphin Imaging software was used for DM cephalometric analysis, and the WebCeph platform was used for AI-based cephalometric analysis. In total, 10 linear and 12 angular measurements were evaluated. The paired t-test, one-way ANOVA test, and intraclass correlation coefficient tests were used to evaluate the differences between the two methods. The level of statistical significance was set at p< 0.05. Results: Except for SNB, NPog, U1.SN, U1.NA, L1-APog, I/I, and LLE parameters, all other parameters presented significant differences between the two methods (p< 0.05). While there was no difference (p> 0.05) in both SNA and SNB measurements between the two methods in the Class I malocclusion group, there was a difference between both methods in the Class II malocclusion group. Meanwhile, only the SNA in the Class III malocclusion group was different (p< 0.05). The ANB angle differed significantly in all three malocclusion groups. For both methods, all parameters except CoA and CoGn were found to have good correlation. Conclusion: Although significant differences were detected in some measurements between the two cephalometric analysis methods, not all differences were clinically significant. The AI-based cephalometric analysis method needs to be developed for more specific malocclusions.

RESUMO Objetivo: Comparar as medidas realizadas usando análise cefalométrica digital-manual (DM) e análise cefalométrica automatizada por meio de uma plataforma online de inteligência artificial (IA), segundo as diferentes más oclusões esqueléticas sagitais. Métodos: Foram incluídas radiografias cefalométricas de 105 indivíduos selecionados aleatoriamente (idade média: 17,25 ± 1,87 anos). O software Dolphin Imaging foi utilizado para análise cefalométrica DM, e a plataforma WebCeph foi utilizada para análise cefalométrica baseada em IA. No total, foram avaliadas 10 medidas lineares e 12 angulares. O testet pareado, a ANOVA de uma via e o coeficiente de correlação intraclasse foram utilizados para avaliar as diferenças entre os dois métodos. O nível de significância foi estabelecido em p<0,05. Resultados: Com exceção dos parâmetros SNB, NPog, U1.SN, U1.NA, L1-APog, I/I e LIE, todos os outros parâmetros apresentaram valores significativamente diferentes entre os dois métodos (p<0,05). Enquanto não foi encontrada diferença (p>0,05) nas medidas SNA e SNB entre os dois métodos no grupo má oclusão de Classe I, foi encontrada diferença entre os métodos no grupo má oclusão de Classe II. Entretanto, no grupo má oclusão de Classe III somente o SNA foi diferente (p<0,05). O ângulo ANB diferiu significativamente em todos os três grupos de más oclusões. Foi encontrada boa correlação entre os dois métodos para todos os parâmetros, exceto para CoA e CoGn. Conclusão: Embora para algumas medidas tenham sido detectadas diferenças significativas entre os dois métodos de análise cefalométrica, nem todas as diferenças foram clinicamente significativas. O método de análise cefalométrica baseado em IA precisa ser aperfeiçoado, com maior especificidade para cada má oclusão.