Thermographic examination of gingival phenotypes: correlation between morphological and thermal parameters.

OBJECTIVES: To evaluate the clinical and thermographic aspects of the gingival phenotype (GP) in healthy subjects. MATERIALS AND METHODS: The study sample examined 264 teeth, upper and lower incisors, comprising 132 central incisors (CI) and 132 lateral incisors (LI), in 33 healthy volunteers. Four periodontal parameters were recorded systematically: ratio of width to length of the dental crown (CW/CL), height of the gingival attachment (KGW), probing depth (PD), and gingival transparency (GT). The temperatures of the attached gingiva (KGW temp) and the free gingival margin (FGM Temp) were also recorded by way of infrared thermography (IRT). RESULTS: The average age of the sample was 30.70 ± 7.65 years. Of the 264 teeth evaluated, 76.1% had a thin GP. There was a significant association between CW/CL (p < 0.001), KGW (p < 0.001), PD (p < 0.007), and FGM Temp (p < 0.006) with the tooth groups. The results show a significant and inversely proportional correlation between clinical parameters and gingival temperature (p < 0.05). A significant association was found between CW/CL (p < 0.026); KGW (p < 0.001); and GP. CONCLUSION: CW/CL, KGW, PD, and FGM Temp vary according to tooth group. The majority of the sample presented with the thin GP, which was more prevalent in the lower LIs and CIs. No significant association could be observed between thermographic parameters and GP. CLINICAL RELEVANCE: Knowledge of the temperature of the gingival tissues can be useful in helping to diagnose and plan periodontal treatments. Moreover, our findings will help future studies evaluate the use of IR as an auxiliary diagnostic method in dentistry, eliminating GP as a confounding factor, since it does not seem to influence the temperature of the gingival tissues.

Age determination on panoramic radiographs using the Kvaal method with the aid of artificial intelligence.

OBJECTIVES: This study aimed to assess and compare age estimation on panoramic radiography using the Kvaal method and machine learning (ML). METHODS AND MATERIALS: 554 panoramic radiographs were selected from a Brazilian practice. To estimate age using the Kvaal method, the following measurements were performed on the upper left central incisors and canines: tooth, pulp and root length; root and pulp width at three different levels: at the enamel-cementum junction (ECJ); midpoint between the enamel-cementum junction and; at the mid root level. For ML age estimation, radiomic, semantic and the radiomic-semantic attribute extractions were assessed. Nineteen semantic and 14 radiomic attributes and a single set of 33 semantic-radiomic attributes were extracted. Logistic Regression, Linear Regression, KNN, SVR, Decision Tree Reg, Random Forest Reg, Gradient Boost Reg e XG Boosting Reg were used for ML classification. For the Kvaal method, Mann-Whitney test, Spearman correlation coefficient, Student's t-test and linear regression with its respective coefficient of determination were used to estimate age and to assess data variability. RESULTS: Mean absolute error (MAE) and standard error estimate (SEE) were assessed. For the Kvaal method, upper incisors presented higher precision than canines (R²: 0.335, SSE: 7.108). Males presented better MAE and SEE values (5.29,6.96) than females (5.69,7.37). The radiomic-semantic attributes presented superior precision (MAE: 4.77) than the radiomic and semantic (MAE: 5.23) attributes. The XG Boosting Reg classifier performed better than the other six assessed classifiers (MAE: 4.65). ML (MAE: 4.77 presented higher age estimation precision than the Kvaal method (MAE: 5.68). CONCLUSION: The use of ML on panoramic radiographs can improve age estimation.