Methods to assess tooth gingival thickness and diagnose gingival phenotypes: A systematic review

医学 重复性 牙科 超声波 后牙 梅德林 科克伦图书馆 荟萃分析 口腔正畸科 放射科 病理 统计 数学 政治学 法学
作者
Jiangyue Wang,Sa Cha,Qing Zhao,Ding Bai
出处
期刊:Journal of Esthetic and Restorative Dentistry [Wiley]
卷期号:34 (4): 620-632 被引量:18
标识
DOI:10.1111/jerd.12900
摘要

Measurement of the periodontal soft tissue dimension is crucial for clinical decision-making and aesthetic prognosis. However, the effectiveness of different measuring methods remains unclear. This systematic review aimed to explore the diagnostic accuracy of two non-invasive methods (namely CBCT and ultrasound) for gingival thickness measurement at different tooth positions.A systematic search was performed using PubMed (including Medline), PubMed Central, OVID, Cochrane Library, LILACS and OpenGrey. Studies focusing on comparisons between CBCT, ultrasound and direct transgingival probing were included. The means, SDs and correlation coefficients with 95% confidence intervals were extracted and analyzed using Review Manager and R software.Twelve studies were selected. No significant difference was found between CBCT measurement and transgingival probing in the anterior and posterior dentition, and a moderate correlation was observed between these two methods (r = 0.41). A weak correlation was found between ultrasound measurement and transgingival probing (r = 0.32), and a slight but statistically significant difference was found when comparing ultrasonic devices and transgingival probing in the posterior area.CBCT can be considered a relatively reliable method for gingival thickness measurement in both the anterior and posterior areas compared with direct probing. Ultrasonic devices provide limited accuracy in the posterior area but are relatively comparable with direct clinical assessments in the anterior area.Measurement location may affect the diagnostic accuracy and repeatability of gingival thickness measurements. Appropriate method selection in different clinical scenarios is crucial to aesthetic outcome prediction and decision-making.
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