Ultrasonographic and cephalometric assessment of tongue thickness across angle’s classes of malocclusion in skeletal class I patients

The aim of this study was to investigate the association between tongue thickness and dental malocclusions, to compare the diagnostic reliability of ultrasonography (USG) and lateral cephalometry in tongue assessment, and explore potential sex-related differences in tongue morphology. Ninety participants (54 females, 36 males; mean age: 16.01 ± 2.26 years) with a skeletal Class I relationship were included and divided into three groups according to Angle’s classification: Class I, Class II, and Class III. Tongue thickness measurements were obtained from lateral cephalometric radiographs as cephalometric tongue thickness (CTT) and from USG as ultrasonographic tongue thickness (UTT). Intraobserver reliability was evaluated using Dahlberg’s formula and Cronbach’s alpha. Statistical analyses included one-way ANOVA, independent-samples t-tests, the Wilcoxon signed-rank test, and Pearson correlation analysis. Statistical significance was set at p < 0.05. Tongue thickness differed significantly among dental malocclusion groups (Class III> Class I > Class II; p < 0.001). For UTT, mean ± SD values were 35.97 ± 2.6 mm for Class I, 35.18 ± 2.2 mm for Class II, and 40.81 ± 1.9 mm for Class III. For CTT, mean ± SD values were 34.97 ± 4.6 mm for Class I, 31.63 ± 3.1 mm for Class II, and 39.06 ± 3.4 mm for Class III. UTT showed higher precision and lower variability than CTT (mean absolute difference: 0.32 mm vs. 0.75 mm; p = 0.002). Males had significantly greater tongue thickness than females (p < 0.05). Correlations between CTT and UTT within malocclusion groups were positive but not significant. Tongue thickness is significantly associated with sagittal dental malocclusion. USG provides a reliable and reproducible method for tongue assessment, demonstrating superior precision compared with lateral cephalometry. Incorporating ultrasonographic evaluation of the tongue into orthodontic planning may facilitate individualized treatment strategies and improve the predictability of clinical outcomes.