Journal of dentistry · 2025
A comparative study on dynamic occlusal adjustment based on mandibular movement trajectories and virtual articulators with different parameters.
СтатьяСравнительное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Yu Meiqi, Ma Liya, Wu Jiacheng, Zhang Yanyan, Liu Weicai, Shen Jiefei
Аннотация
OBJECTIVES: This study aimed to compare the performance of different dynamic occlusal adjustment methods.
METHODS: Occlusal adjustment was performed on 20 single crowns (SC) and 20 three-unit splinted crowns (TUC), each with intentional 0.3 mm occlusal interferences introduced using Meshmixer. Restorations were adjusted in computer-aided-design software (Exocad, Germany) using three different dynamic occlusal adjustment methods: virtual articulator with default parameters (VAD), virtual articulator with individual parameters (VAI), and mandibular movement trajectories (MMT). The adjusted and original models were imported into Geomagic, where the occlusal surfaces were extracted and compared using closest‑point distance mapping to calculate the mean deviation (MD), root mean square (RMS), and the proportion of points within deviation ranges. A two-way analysis of variance (ANOVA) was conducted to evaluate the effects of adjustment method, restoration type, and their interaction. When significant variations were detected, one-way ANOVA with least significant difference or Dunnett's T3 test was applied for multiple comparisons (α=0.05).
RESULTS: For both types of restorations, MMT exhibited significantly lower MD values (SC: 0.19 ± 0.05 mm; TUC: 0.18 ± 0.05 mm) than VAD and VAI (p<0.05). RMS followed the same trend (p<0.05), with no significant difference observed between VAD and VAI (p>0.05). MMT also demonstrated the highest proportions of distance deviation within the -0.1-0 mm and 0-0.1 mm ranges compared with VAD and VAI (p<0.01).
CONCLUSIONS: Different dynamic occlusal adjustment methods significantly influenced the occlusal design of posterior SCs and TUCs. Specifically, the MMT-based adjustment achieved higher occlusal accuracy than both VAI and VAD.
CLINICAL SIGNIFICANCE: These findings underscore the advantage of integrating patient-specific mandibular movement trajectories into digital workflows, suggesting that real-time jaw-tracking data could further refine occlusal adjustment performance.
METHODS: Occlusal adjustment was performed on 20 single crowns (SC) and 20 three-unit splinted crowns (TUC), each with intentional 0.3 mm occlusal interferences introduced using Meshmixer. Restorations were adjusted in computer-aided-design software (Exocad, Germany) using three different dynamic occlusal adjustment methods: virtual articulator with default parameters (VAD), virtual articulator with individual parameters (VAI), and mandibular movement trajectories (MMT). The adjusted and original models were imported into Geomagic, where the occlusal surfaces were extracted and compared using closest‑point distance mapping to calculate the mean deviation (MD), root mean square (RMS), and the proportion of points within deviation ranges. A two-way analysis of variance (ANOVA) was conducted to evaluate the effects of adjustment method, restoration type, and their interaction. When significant variations were detected, one-way ANOVA with least significant difference or Dunnett's T3 test was applied for multiple comparisons (α=0.05).
RESULTS: For both types of restorations, MMT exhibited significantly lower MD values (SC: 0.19 ± 0.05 mm; TUC: 0.18 ± 0.05 mm) than VAD and VAI (p<0.05). RMS followed the same trend (p<0.05), with no significant difference observed between VAD and VAI (p>0.05). MMT also demonstrated the highest proportions of distance deviation within the -0.1-0 mm and 0-0.1 mm ranges compared with VAD and VAI (p<0.01).
CONCLUSIONS: Different dynamic occlusal adjustment methods significantly influenced the occlusal design of posterior SCs and TUCs. Specifically, the MMT-based adjustment achieved higher occlusal accuracy than both VAI and VAD.
CLINICAL SIGNIFICANCE: These findings underscore the advantage of integrating patient-specific mandibular movement trajectories into digital workflows, suggesting that real-time jaw-tracking data could further refine occlusal adjustment performance.