Multiple regression models for age estimation by assessment of morphologic dental changes according to teeth source

The aims of this study were twofold: (1). to measure parameters that contribute significantly to estimates of dental age, using a combination of classic methods and a computer-assisted image analysis procedure to avoid the bias inherent in observer subjectivity; and (2). to develop new mathematical regression models for age prediction according to postmortem interval. Two different populations were studied. Forty-three permanent teeth (Group I), extracted for valid clinical reasons, were taken from patients 25-79 years of age. The other population group (Group II) was composed of 37 healthy erupted permanent teeth obtained from human skeletal remains (age 22-82 years) with a postmortem interval ranging from 21 to 37 years. Morphologic age-related changes were investigated by measuring variables on intact and half-sectioned teeth. Multiple regression analyses were performed with age as the dependent variable for each sample source. In fresh extracted teeth, the variables that made the greatest contributions to predictions of age were dental attrition, dentin color, and translucency width, the latter measured with a computer-assisted image analysis method. In teeth from human skeletal remains, the variables that made the greatest contributions to age calculation were cementum apposition, pulp length measured by computer-assisted image analysis, dental attrition, root translucency, and dental color. We conclude by recommending different regression models to calculate age depending on the postmortem interval.