ISSN 2413-6077. IJMMR 2017 Vol. 3 Issue 234 D e n t is t r y dOI 10.11603/IJMMR.2413-6077.2017.2.8228 bIOmETRIc mEThOd Of AgE EsTImATION: dEVELOPmENT ANd EffIcIENcY, IN cAsEs Of PAThOLOgIEs Of TEETh hARd TIssUEs 1M. Yu. Goncharuk-Khomyn, 2Kh. V. Pohoretska, 2L. O. Patskan 1UZHHOROD NATIONAL UNIVERSITY, UZHHOROD, UKRAINE 2I. HORBACHEVSKY TERNOPIL STATE MEDICAL UNIVERSITY, TERNOPIL, UKRAINE Background. The physiological changes of tooth are the criteria used in evaluation of regressive formula by Kvaal et al. age estimation technique. But in cases of abnormal occlusion, abnormal chewing habits, bruxism, abrasive factors or structural defects of teeth the intensity of tooth aging accelerates. Objective. The aim of the research was to define the options of age estimation according to dental state of individuals with pathological attrition. Methods. 108 panoramic x-ray photos of patients with pathological attrition of teeth were chosen by a randomized selection (49 males and 59 females). All photos were made by means of Planmeca PROMAX orthop- antomograph. Nine measurements were made for each tooth: the tooth length, pulp length, root length, root width and pulp width at three different levels: cement-enamel junction (level A, beginning of root), one-quarter of root length from a cement-enamel junction (level B), and mid-root (level C). Due to these measurements, a number of ratios were calculated in accordance with Kvaal et al. method. Results. The errors that reached 27±8.4 years were found when evaluating the dental age using primary coefficients of equations suggested by the authors of the method used. By means of mathematical analyses, principal component regression method as well, the correlation coefficient of Pearson and method of combining linear regression due to the tooth changes in cases of pathological attrition (lowering level of occlusal surface, dystrophy of pulp structures and deposition of tertiary reparative dentine) by regression analysis, the modified formulas for age estimation using radiographic technique were found. Modified coefficients decreased the error to 13±0.8 years, which was relative to the real age upto nearly 42–48% compared to the primary coefficients of equations for pathological attrition. Conclusions. Age estimation technique can be improved taking into account morphological changes in pathological attrition and the calculated coefficients make it possible to expand the circle of person’s age which needs to be found. KEY WORDS: age estimation; Kvaal method; pathological attrition; regression analyses; reparative dentine deposition. Corresponding author: Myroslav Goncharuk-Khomyn, Depart- ment of Prosthetic Dentistry, Uzhhorod National University, 16/a Universytetska Str., Uzhhorod, Ukraine, 88000 Phone number: +380991212813 E-mail: myroslav.goncharuk-khomyn@uzhnu.edu.ua Introduction Estimation of biological age of a person is significant in forensic science, especially for comparative and reconstructive identification antemortem and postmortem as recommend- ed by Interpol/ICPO (International Criminal Police Organization) and FBI (Federal Bureau of Investigation). Justice bodies in an ethni- cally heterogeneous society use the results of age estimation by dental status where age in- dicators affect the need of socio-vulnerable persons, illegal immigrants and children and allow benefiting from the state budget; it also influences on level of criminal responsibility of persons with regard to age limit [1]. Age is the least variable and probably the most accurate determining parameter, since the aging pro- cess is the most independently reflected in changes of pulp and hard tissues of teeth than any other functional system of the body that is more vulnerable to the effects of pathologal features, constitution and physiological defects. Practical determination of the age of adults is possible using morphological techniques of Gustafson G. (“Age determination on teeth”), Bang G., Ramm E. (“Determination of age in humans from root dentine transparency”) Jo- hanson G. (“Age determination from teeth”), Maples W. R. (“An improved technique using dental histology for estimation of adult age”), International Journal of Medicine and Medical Research 2017, Volume 3, Issue 2, p. 34–38 copyright © 2017, TSMU, All Rights Reserved M. yu. goncharuk-Khomyn et al. ISSN 2413-6077. IJMMR 2017 Vol. 3 Issue 2 35 D e n t is t r y M. yu. goncharuk-Khomyn et al. and morphologically-radiographic techniques of Solheim T. (“A new method for dental age estimation in adults”), Kvaal S. I. et al (“Age esti mation of adults from dental radiographs”) [2–10]. The most rational method which ex- cludes extraction of teeth and subjective grad- ing of morphological indicators is Kvaal et al. technique, which involves calculating the ratio of length of crown and root to the length of pulp, width of root to the width of pulp in spe- cifically designated areas, searches averages and uses standardized coefficients for the final result. However, this technique does not pro- vide the effective use in cases of the presence of hard tissue lesions of teeth, pathological attrition is the most common. Attrition is a constant form of retrogressive changes in teeth, which involves lowering the level of oc- clusal surface in the amount related to the normal process of aging. The physiological loss of hard tissue caused by tooth-to-tooth contact in occlusion and mastification depends on diet, dentition, force of mastificatory muscles and chewing habits. Physiological attrition is pro- portional to the age of an individual as deposi- tion of secondary dentine or pulp changes during lifetime [11–13]. The physiological changes of tooth are the criteria used in calcu- lation of regressive formula by Kvaal et al. age estimation technique. But in cases of abnormal occlusion (crowding of teeth, malposed teeth, lesions of prosthetics treatment), abnormal chewing habits, bruxism, abrasive factors or structural defects in teeth take place the inten- sity of tooth aging increases. This process is pathological attrition. We have found out that in pathological attrition the occlusal level of teeth lowers in a few times faster in depen- dence of forces that influence. Formation of reparative tertiary dentine, closing volume of pulp chamber and dystrophy processes take place in pulp structure that is unusual for physiological attrition [14–20]. Due to all these factors and principles we have approbated primary method of Kvaal et al. age estimation technique and found modified regression for- mulas that approximate the calculation results with the real age. Methods 108 panoramic X-rays photo of patients with pathological attrition of teeth were chosen by a randomized selection (49 males and 59 fe- males). All photos were made by means of Planmeca PROMAX orthopantomograph [14– 15]. Using graphical redactor Adobe Photoshop CS3 some teeth were cut from each panoramic photo: maxillary central incisor, lateral incisor and second premolar, mandibular later incisor, canine and first premolar, and all of them were positioned strongly on vertical axis. Nine mea- surements of each tooth were made: tooth length, pulp length, root length, root width and pulp width at three different levels: cement­ enamel junction – (level A, beginning of root), one-quarter of the root length from the ce- ment-enamel junction (level B), and mid-root (level C) [10]. All measurements were per- formed by means of Measurement tool in Adobe Photoshop CS3 mainly in pixels and then converted into millimeters. For each value 7 measurements were made and for further calculation the average means were estimated (Table 1). From the measurements, a number of ratios were calculated in accordance with Kvaal et al.: P – the ratio of pulp length to root length; T – the ratio of tooth length to root length; R – the ratio of pulp length to tooth length; A – the ratio of width of pulp to root at level A; B – the ratio of width of pulp to root at level B; C – the ratio of width of pulp to root at level C; M – the mean values of all ratios; W – the mean value of width ratios from levels B and C; L – the mean value of length ratios P and R; W-L – the difference between W and L [10]. All ratios were calculated using standard Microsoft Office program package Microsoft Office Excel. The mean of all ratios (M) was used as the first predictor, while the difference between the mean of the 2 width ratios and the mean of the 2 length ratios (W-L) was used as the second predictor. The results of estimated age differ significantly from real age and reach the error up to nearly 48%. Using correlation coefficient and principal component regression method it was established that the strongest correlation between age results of patients with pathological attrition and calcu- la ted ratios was evidenced between R and further L, W, and also A (as the level closest to the centers of reparative dentine formation), which occurred because of significantly different level of tooth loss and closing volume of pulp chamber due to attrition (Table 2). The statistical information was analyzed by means of Statystics Pro software and linear regression analysis, thus new modified coefficients for Kvaal et al. primary formulas were estimated (Table 3). As a result, the error reached 13±0.9 years and did not increase more. ISSN 2413-6077. IJMMR 2017 Vol. 3 Issue 236 D e n t is t r y Results The most significant correlation results between tooth and age were established in upper and lower incisors, and lower premolar. The lowest correlation was found at lower ca- nine in patients with pathological attrition be- cause of the level of influence of pathological attrition on different types of tooth. The Pear- son correlation coefficients between chrono- logical age and different ratios (P, T, R, A, B, C) calculated due to the length and width mea- surements on the orthopantomographs are displayed in Table 2. The differences compared to primary correlation are significant at R, L, W Table 1. Example of measurement of tooth specific indicators of patient with pathological attrition Specific parameters Base/repeat measurements Measurements and calculations Number of measurements Mean (mm) Difference (mm) Tooth length Main measurement 7 22.07 0.03Repeated measurement 7 22.10 Pulp length Main measurement 7 17.02 0.47Repeated measurement 7 16.55 Root length Main measurement 7 15.00 0.29Repeated measurement 7 14.71 Pulp width A Main measurement 7 0.82 0.01Repeated measurement 7 0.81 Pulp width B Main measurement 60 0.72 0.02Repeated measurement 60 0.70 Pulp width C Main measurement 60 0.39 0.01Repeated measurement 60 0.40 Root width A Main measurement 60 5.14 0.04Repeated measurement 60 5.10 Root width B Main measurement 60 4.36 0.07Repeated measurement 60 4.29 Root width C Main measurement 60 3.82 0.07Repeated measurement 60 3.75 Table 2. Correlation between age of patients with pathological attrition and the ratios of measurements Upper central incisor Upper lateral incisor Upper second premolar Lower lateral incisor Lover canine Lower first premolar P -0.11 -0.08 -0.16 -0.15 -0.07 -0.49 T -0.34 -0.07 -0.11 -0.12 -0.16 -0.44 R 0.24 -0.14 -0.16 -0.12 -0.04 -0.28 A -0.19 -0.30 -0.16 -0.22 -0.90 -0.10 B -0.30 -0.20 -0.16 -0.32 -0.14 -0.20 C -0.32 -0.30 -0.27 -0.31 -0.15 -0.20 M -0.31 -0.26 -0.21 -0.34 -0.17 -0.39 L -0.08 -0.11 -0.17 -0.27 -0.14 -0.23 W-L -0.39 -0.14 -0.08 -0.30 -0.02 0.21 Table 3. Modified regression equations of patients with pathological attrition Equation R2 (Coefficient of determination) Significant predictors All six teeth Age=45.1+5.42 M+3.76 W-L 0.21 None Lower canine Age=77.1–84.1 M–51.09 W-L 0.012 None Lower lateral incisor Age=24.6+4.06 M–19.01 W-L 0.04 None Lower first premolar Age=–21.4+16.5 M–36.1 W-L 0.356 M and W-L Upper second premolar Age=125.6–84.02 M+42.4 W-L 0.211 W-L Upper lateral incisor Age=35.11–16.5 M–38.1 W-L 0.214 None Upper central incisor Age=35.6–76.8 M–56.3 W-L 0.27 W-L Upper three teeth Age=30.14+14.7 M+2.10 W-L 0.045 M Lower three teeth Age=19.2+5.7 M–12.18 W-L 0.051 W-L M. yu. goncharuk-Khomyn et al. ISSN 2413-6077. IJMMR 2017 Vol. 3 Issue 2 37 D e n t is t r y M. yu. goncharuk-Khomyn et al. and A ratios because of specific processes in teeth in cases of pathological attrition. By re- gression analysis new formulas were developed (Table 3) and the levels of absolute and relative errors were compared (Table 4). Discussion The results depend on the stage of patho- logical attrition. In the research we have found out that the attrition is caused by bruxism, abnormal occlusion due to dispositioned tooth, and inadequate prosthetic treatment may cause proportional constant intense deposition of tertiary reparative dentine and lowering of occlusal surface depending on pathology stage [12]. However, due to abnormal tooth structures or abrasion factors pathological attrition is not a progressive process during which pulp struc- tures and hard tissues changes depend on time and stage of changes and enhanced process development may take place any time. Also, the better result were established when the mean levels of all six teeth were cal culated, and the most distant result were reached when single measurements of mandible canine were inclu- ded. Improved regressive formulas were che- cked by new randomized samples of 50 X-ray photos of patients with pathological attri tion, no information about age was available. The results were ranged by the error not higher than 14±0.8 years old. The further research should be focused on verifying value of tertiary reparative dentine using computer cone beam tomography to de- termine the dynamics of pulp changes intensity in different stages of disorders that cause pa- thological attrition. It allows making retro spec- tive analysis, which provides information about average changes of pulp chamber and hard tissues of tooth, so identification of phy siolo­ gical secondary dentine formation before the pathology come about and tertiary repa rative dentine formation in pathology develop ment gives a chance to create new regression analy- sis by two systems coefficients ‘before patho­ logy’ and ‘during pathology development’. Thus all minimal errors occur in techniques of age de ter mination in cases of dental health disorders. Conclusions During the study we used Kvaal et al. age estimation technique for patients with patho- logical attrition and defined the level of errors which reaches about 47-49%. Using component regression analysis and Pearson’s coefficients we determined the correlation between age results and level of tooth surface attrition and deposition of tertiary reparative dentine due to the kind of pathology, which cause pathological attrition, and the time of pathology. The most significant correlation was found between changes in incisors and lower first premolar. The changes in canine in cases of pathology attrition do not affect the result significantly. Individually calculated modified coefficients in equations by Kvaal et al. age estimation tech- nique in cases of pathological attrition showed the result more adjacent to real age, e.g. the level of absolute error in years was improved from 27±8.4 to 13±0.8 years. Age estimation technique can be improved taking into account morphological changes in cases of pathological attrition, and the calcu- lated coefficients allow expanding the circle of person’s age that should be defined. Also the examination of two regression systems which stand for attrition of occlusal surface and ter- tiary reparative dentine deposition in cases of pathology (system 1) and lowering of physio- logical lever of tooth high as well as deposition of secondary dentine (system 2) exclude the number of errors for accurate age estimation with cone beam tomography. Table 4. Example of differences of age estimated by means of primary technique of Kvaal et al. and modified technique for patient A with pathological attrition Teeth/tooth groups Age Primary technique of Kvaal et al. for patients with pathological attrition Modified technique of Kvaal et al. for patients with pathological attrition Mean Difference Mean Difference Single tooth Actual age 35 21 35 13Estimated age 56 48 All six teeth Actual age 35 16 35 10Estimated age 51 45 Three maxillary teeth Actual age 35 18 35 12Estimated age 53 47 Three mandibular teeth Actual age 35 17 35 14Estimated age 52 39 ISSN 2413-6077. IJMMR 2017 Vol. 3 Issue 238 D e n t is t r y References 1. Amandeep S. Age estimation from physiological changes of teeth. J Indian Forensic Sci. 2004;6(2): 113–121. 2. Willems G. A review of commonly used dental age estimation techniques. J. Forensic Odonto- stomatol. 2001;19(1):9–17. 3. Solheim T, Sundnes PK. 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