Abstract
Quite recently, the number of pediatric patients with temporomandibualr disorders (TMD) seems to be increasing. The reasons for the disorders, however, are still unknown. The bony structures constructing the temporomandibular joint (TMJ) may influence an occurrence of the disorders. The aim of this present study was to analyze the changes of the glenoid fossa during pediatric period using computed tomography (CT) data obtained from selected patients who were imaged for other diagnostic purposes over the maxilla and/or mandible regions. The patients consisted of 72 joints in 36 patients (18 males and 18 females) whose age was between 7 and 12 years. None of the patients had growth abnormalities, such as facial growth disturbance. CT three-dimensional images (3 D images)were constructed according to the so-called“Volume Rendering Technique”. Five subjects were established to analyze the changes of the glenoid fossa; in the vertical dimension, in the anterior to posterior dimension, in the medial to lateral dimension, the location in the axial plane and the angle of the glenoid fossa. To represent the 5 subjects, measuring values were calculated in the 3 D images where axial, coronal and saggital aspects can be observed. For statistical analysis, Spearman's correlation coefficient was used to analyze the correlation between age and the measuring values. Simple linear regression was also done to obtain a linear regression model between them. Of these values, almost all showed significant correlation coefficients with age whereas the values in the anterior to posterior dimension did not. Of the values obtained with significant coefficients, the values in the vertical dimension did not obtain significant linear regression models whereas the remaining values did. The results from the present study suggested that the changes of the glenoid fossa during the pediatric period varied in three dimensions and that further studies are needed to specify the normal growth pattern with other statistical methods, such as logistic regression analysis.
