Dental age estimation and different predictive ability of various tooth types in the Czech population: data mining methods.
Dental development is frequently used to estimate age in many anthropological specializations. The aim of this study was to extract an accurate predictive age system for the Czech population and to discover any different predictive ability of various tooth types and their ontogenetic stability during infancy and adolescence. A cross-sectional panoramic X-ray study was based on developmental stages assessment of mandibular teeth (Moorrees et al. 1963) using 1393 individuals aged from 3 to 17 years. Data mining methods were used for dental age estimation. These are based on nonlinear relationships between the predicted age and data sets. Compared with other tested predictive models, the GAME method predicted age with the highest accuracy. Age-interval estimations between the 10th and 90th percentiles ranged from -1.06 to +1.01 years in girls and from -1.13 to +1.20 in boys. Accuracy was expressed by RMS error, which is the average deviation between estimated and chronological age. The predictive value of individual teeth changed during the investigated period from 3 to 17 years. When we evaluated the whole period, the second molars exhibited the best predictive ability. When evaluating partial age periods, we found that the accuracy of biological age prediction declines with increasing age (from 0.52 to 1.20 years in girls and from 0.62 to 1.22 years in boys) and that the predictive importance of tooth types changes, depending on variability and the number of developmental stages in the age interval. GAME is a promising tool for age-interval estimation studies as they can provide reliable predictive models.
Department of Anthropology and Human Genetics, Faculty of Science, Charles University in Prague, Prague, Czech Republic. velemins@natur.cuni.cz
Jana
J
Velemínská
Computational Intelligence Research Group, Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic.
Ales
A
Pilný
Computational Intelligence Research Group, Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic.
Miroslav
M
Cepek
Stomatologic Clinic, 3rd Medical Faculty and Faculty Hospital Královské Vinohrady, Charles University in Prague, Prague, Czech Republic.
Magdaléna
M
Kot'ová
Department of Anthropology and Human Genetics, Faculty of Science, Charles University in Prague, Prague, Czech Republic.
Radka
R
Kubelková
D000293:Adolescent; D000366:Age Determination by Teeth / Q000379:methods*; D002648:Child; D002675:Child, Preschool; D003430:Cross-Sectional Studies; D057225:Data Mining / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D011862:Radiography, Panoramic; D012044:Regression Analysis; D060388:Support Vector Machine; D014070:Tooth / Q000033:anatomy & histology* / Q000000981:diagnostic imaging