Abstract Objectives To determine and compare the differences in dental age (DA) and chronological age (CA) of Demirjian and Willems methods in 9- to 11-year-old Caucasian boys and girls born with non-syndromic unilateral cleft lip and palate (NSUCLP) to an age-matched control group. Analysis of the results is undertaken to determine whether there are differences between gender, groups, and methods. Materials and methods Dental panoramic tomograph (DPT) of 108 children with NSUCLP aged from 8.79 to 10.99 years (x¯=10.05 ± 0.36) were compared to 107 control children. Age, gender, and group were blinded during examination. The Demirjian and Willems methods were used by both authors to visually examine the radiographs. Differences between DA and CA were determined with a repeated two-way ANOVA. Results Inter-examiner reliability was good (ICC ≈ 0.75). For each of the methods used, the mean DA/CA difference was greater in the control group than in the NSUCLP group (P = 0.011). The control group’s Demirjian mean DA/CA difference was 1.08 years and Willems mean was 0.46 years, compared to the NSUCLP group’s Demirjian mean DA/CA difference 0.83 years and Willems mean 0.22 years. Only the Willems method determined a statistically significant gender difference (P = 0.000). Conclusions The null hypothesis was rejected. Willem’s method as compared to Demirjian’s displayed greater accuracy in estimating CA. Both methods overestimated CA but detected DA delay in NSUCLP children compared to the control group. However, the methods were inconsistent in estimating gender CA. Introduction A multitude of factors, including developmental, environmental, and genetic, influence the physical growth and maturity of the different organs of children. The complex combination and interaction of these factors results in variations in growth between children of the same age and leads to discrepancies between a child’s chronological age (CA) and actual developmental age (1). Diagnostic capability and appropriately to time orthodontic intervention can be improved if the actual maturity age can be reliably estimated. This gap drives the need to devise a reliable maturity age index. A number of indices have indeed been introduced, which address the secondary sex characteristics, skeletal maturity, and dental development. Having reliable estimates of dental age (DA) and development has applications in formulating orthodontic treatment plans as well as contributing to forensic anthropology and being useful to estimate the age of skeletal remains. Furthermore, DA can be used to estimate the age of children who lack a birth record (2–4). Originally, DA estimation was based on evaluating dental eruption (5, 6); however, this is not a reliable method as teeth eruption is influenced by dental crowding, premature or delayed loss of primary teeth, impacted teeth, syndromes, and tumors (7). Furthermore, this method of estimating DA depends on active dental eruption (8). Dental mineralization is an alternative, more accurate method of determining DA (9). Among the various methods devised to determine DA, Demirjian et al. (8) is prevalent in the literature. Published in 1973, this method was created using dental panoramic tomographs (DPTs) collected from French/Canadian children to evaluate the development of the seven left permanent mandibular teeth (excluding third molars). The method involves matching each tooth to one of the eight developmental stages, each of which is designated with a score. The DA is calculated by taking the sum of the developmental stage scores of the seven teeth of interest and plotting it against conversion tables or growth charts devised by Demirjian et al. (8). Although this method has the advantage of being simple, it is vulnerable to overestimating DA in other populations (10–14). A modified method is offered by Willems et al. (11), which used a Belgian sample to adapt the score tables; this alternative method has demonstrated superior-DA estimating capability (2, 5–17). The most common congenital defect of the head and neck is cleft lip and/or cleft palate (CL/P), which may be either unilateral or bilateral (18–20); incidence rates vary widely between different countries, ethnicities, and races. In Asian and Native American populations, CL/P occurs in approximately 1 in 500 births, making it twice as common as its incidence in most European countries (1 in 1000) and five times more common than in African populations (1 in 2500) (21, 22). Interestingly, Hagberg et al. (23) found Sweden’s CL/P incidence to be similar to Asia’s (1 in 500 births) and for it to be more prevalent in males than females. Studies of children with CL/P indicate they are more vulnerable to dental abnormalities compared to children without the cleft defect (24, 25). The extent of dental abnormality correlates with the severity of the cleft and in unilateral cleft defects; dental abnormality is more common in the cleft side. Tooth agenesis, enamel hypoplasia, abnormal tooth formation, irregular size, and eruption are abnormalities frequently present in CL/P (24). The formation of permanent teeth in both jaws is delayed in children with CL/P compared to those without CL/P (24–28). To the best of our knowledge, as yet there is no study that uses a control and CL/P group to compare the effectiveness of the Demirjian and Willems methods. This study has been devised to determine and compare the difference between DA and CA of Demirjian and Willems methods in 9- to 11-year-old Caucasian boys and girls born with non-syndromic unilateral cleft lip and palate (NSUCLP) and an age-matched control group. The results are analysed to determine whether there are differences between gender, groups, and methods. We hypothesized that there are no differences in DA estimation when using Demirjian and Willems techniques between children born with or without NSUCLP. Materials and methods The Ethical Board of Stockholm approved this study (Daybook no. 2015/1582-31/2). A power analysis was done considering that a 0.3 year difference between CA and DA is statistically significant. Retrospective data covering 1961–2004 were obtained from the Karolinska Institutet’s archive and digital file system. Inclusion criteria were: All subjects should be of Caucasian descendent, Subjects should be born with NSUCLP, No agenesis or teeth extraction outside the cleft area, and DPT was taken when the age of the child was approximately 9–11 years old and in good quality. One hundred and eight DPTs were collected from children with NSUCLP (Table 1). The NSUCLP group comprises 69 boys and 39 girls with a mean CA of 9.94 years ± 0.48 (ranging from 8.79 to 10.99 years). NSUCLP diagnosis was confirmed by file records and all were treated by the Stockholm Craniofacial Team. CL/P were left sided in 62.3 per cent of boys and 74.3 per cent of girls. A control group of 107 children was matched for age (x¯=10.05 ± 0.36) and their CA ranged from 9.06 to 10.81 years (Table 1). DPTs from 77 patients were in an analogue form, which were digitized by an Epson Perfection™ V750-M Pro scanner at 600 dpi resolution. Table 1. Mean ( x¯ ) and SD in years as well as the range of CA of sample of patients (n) born with NSUCLP and a control. NSUCLP, non-syndromic unilateral cleft lip and palate; SD, standard deviation. Group Gender N CA range x¯ SD NSUCLP Male 69 9.02–10.99 10.00 0.52 Female 39 8.79–10.82 9.83 0.40 Control Male 53 9.06–10.81 10.02 0.40 Female 54 9.38–10.67 10.07 0.33 Group Gender N CA range x¯ SD NSUCLP Male 69 9.02–10.99 10.00 0.52 Female 39 8.79–10.82 9.83 0.40 Control Male 53 9.06–10.81 10.02 0.40 Female 54 9.38–10.67 10.07 0.33 View Large To minimize bias, each child’s gender, date of birth, and date of radiograph were masked to examiners and each child was denoted by a number. Each DPT was edited to show only the lower left teeth (excluding third molars). Each DPT was visually examined by both authors in accordance with Demirjian’s and Willems’ methods. Calibration was performed using 25 X-rays randomly selected by ‘Pick Me!’ (by Donation Coder forum, 2009), a free-random selection program. Following an 8-week wash-out period, a further 40 randomly selected X-rays were used to perform a second calibration. As there was consensus between the calibrations and discrepancies had been eliminated, another 6-week wash-out period passed before all the subjects were evaluated over the subsequent 6–8 weeks. To estimate CA, the date of birth was subtracted from the date of the X-ray. To simplify calculating DA, the CA was converted to decimals using a free tool for age calculation (29). The DA scores derived from the Demirjian and Willems methods were calculated, and then subtracted from the converted CA. Statistics This is a retrospective cross-sectional study. Significance level was set to P = 0.05 and all data were analyzed with SPSS software (version 22.0, SPSS Inc., Chicago, Illinois, USA). Inter-examiner reliability was measured using intraclass correlation (ICC) test, a two-way random single measurement (absolute agreement). The reliability between the DA methods and the CA was assessed using the Pearson correlation test. Mean DA/CA difference and standard deviation were calculated as well as the percentage of estimations equal or less than 1 year. A two-way repeated-measurement ANOVA was performed to evaluate the DA/CA differences depending on the DA method used, groups, and gender. Checking model assumptions Our sample fulfilled the normal distribution and the equality of variance requirements for the ANOVA test. There was one single observation deviated from the other measurements that was included in the final analysis. To study the effect of this outlier on the results, a repeated analysis excluding this measurement showed no difference in our results. Hence, the observation could not be held to influence the results of the ANOVA analysis. Moreover, Cook’s distance measurement showed that the highest observed value was not above 0.08, which means that it has no influence over the model (score above 1.0 means a large influence on the results). Levene’s test was also used to test equality of variance. The P-value of Demirjian and Willems DA differences were 0.48 and 0.35, respectively (P > 0.05). So, we cannot assume that there is a violation of variance equality. Results Reliability The agreement between raters was good, ICC ≈ 0.75 (Figure 1) (30). Moreover, the correlation coefficient between DA methods used in both raters was high (r = 0.90). Because of the good inter-examiner reliability, only one author’s (AN) measurements were used to calculate the results. Figure 1. View largeDownload slide Inter-rater agreement of Demirjian and Willems dental age (DA) estimation methods using ICC. ICC, intraclass correlation coefficient test. Figure 1. View largeDownload slide Inter-rater agreement of Demirjian and Willems dental age (DA) estimation methods using ICC. ICC, intraclass correlation coefficient test. DA methods compared to groups As the descriptive statistics for both methods presented in Table 2 indicate, the mean DA/CA difference is greater in the control group than in the NSUCLP group. Demirjian’s mean difference score for the control group was 1.08 ± 0.84 years compared to 0.83 ± 0.73 for the NSUCLP group; Willems’ mean DA/CA difference scores for the control and NSUCLP groups were 0.46 ± 0.83 years and 0.22 ± 0.68 years, respectively (Figure 2). The test of within-subjects effects, i.e. within the same method, showed no interaction between DA methods and groups; however, there was a statistical difference between groups and DA methods in the between-subjects effects test (P = 0.011) (Table 3). Table 2. Mean ( x¯) and SD of DA and CA difference in years estimated by the Demirjian and Willems methods for the (n) of children born with NSUCLP and a control group. The table also shows the comparison between female (F) and male (M) subjects as well as the percentage (%) of mean DA/CA difference estimated equal or less than 1 year. CA, chronological age; DA, dental age; SD, standard deviation. Category Demirjian DA/CA difference (years) Willems DA/CA difference (years) N x¯ SD % n x¯ SD % Group NSUCLP 108 0.83 0.73 52.77 108 0.22 0.68 86.10 Control 107 1.08 0.84 43.92 107 0.46 0.83 68.20 Gender F 93 1.01 0.80 49.46 93 0.20 0.76 78.49 M 122 0.92 0.79 46.72 122 0.45 0.75 76.20 Group NSUCLP F 39 0.92 0.72 48.70 39 0.06 0.66 87.10 M 69 0.78 0.73 53.60 69 0.31 0.68 85.50 Control F 54 1.07 0.85 50.00 54 0.30 0.81 72.20 M 53 1.10 0.83 37.70 53 0.63 0.82 64.20 Category Demirjian DA/CA difference (years) Willems DA/CA difference (years) N x¯ SD % n x¯ SD % Group NSUCLP 108 0.83 0.73 52.77 108 0.22 0.68 86.10 Control 107 1.08 0.84 43.92 107 0.46 0.83 68.20 Gender F 93 1.01 0.80 49.46 93 0.20 0.76 78.49 M 122 0.92 0.79 46.72 122 0.45 0.75 76.20 Group NSUCLP F 39 0.92 0.72 48.70 39 0.06 0.66 87.10 M 69 0.78 0.73 53.60 69 0.31 0.68 85.50 Control F 54 1.07 0.85 50.00 54 0.30 0.81 72.20 M 53 1.10 0.83 37.70 53 0.63 0.82 64.20 View Large Figure 2. View largeDownload slide Difference between DA and chronological age (CA) differences estimated by Demirjian and Willems methods (in years) as compared to the children born with non-syndromic cleft lip and palate and the control sample. Figure 2. View largeDownload slide Difference between DA and chronological age (CA) differences estimated by Demirjian and Willems methods (in years) as compared to the children born with non-syndromic cleft lip and palate and the control sample. Table 3. Effects of within-subjects tests and between-subjects tests for methods, gender, and groups (NSUCLP and control groups). Source Sum of squares df Mean square F-value P-value Within-subject effects Method 42.89 1 42.89 815.28 0.000*** Method × gender 3.10 1 3.10 58.87 0.000*** Method × group 0.04 1 0.04 0.76 0.386 Error (method) 11.15 212 0.05 Between-subject effects Gender 1.49 1 1.49 1.33 0.250 Group 7.41 1 7.41 6.64 0.011* Error 236.57 212 1.12 Source Sum of squares df Mean square F-value P-value Within-subject effects Method 42.89 1 42.89 815.28 0.000*** Method × gender 3.10 1 3.10 58.87 0.000*** Method × group 0.04 1 0.04 0.76 0.386 Error (method) 11.15 212 0.05 Between-subject effects Gender 1.49 1 1.49 1.33 0.250 Group 7.41 1 7.41 6.64 0.011* Error 236.57 212 1.12 The model was tested for interaction between gender and cleft group, but since this effect was not significant this interaction was excluded from the model. P = 0.05 View Large DA methods compared to gender In the Demirjian method, girls in both groups demonstrated greater dental development than the boys; yet this difference was not statistically significant (Table 2). On the other hand, a statistical difference was detected between genders with Willems method. This suggested that the boys’ dental development was more advanced than the girls (Figure 3). The statistically significant interaction between gender and DA methods (P = 0.000) was analysed by pairwise comparisons. First, gender was compared within the two DA methods; in a model adjusting for group, the Demirjian method showed no difference between boys and girls (P = 0.626). Yet, the Willems method showed a statistically significant difference between boys and girls, adjusting for group (P = 0.005). The DA of the girls averaged 0.29 years younger than the boys (Table 4). Second, when comparing DA methods within gender, we found that there was a statistically significant effect of DA methods both within females and males. The Demirjian method overestimated age more than the Willems method for gender groups (P = 0.000). However, the estimated effect is higher in the female group (0.81 years) than the male group (0.46 years) (Table 5). Figure 3. View largeDownload slide Difference between DA and CA differences estimated by Demirjian and Willems methods (in years) as compared to the gender. Figure 3. View largeDownload slide Difference between DA and CA differences estimated by Demirjian and Willems methods (in years) as compared to the gender. Table 4. Comparison of gender [female (F) and male (M)] within the methods of DA estimation by Demirijan and Willems. (Based on estimated marginal means.) Method Gender (A) Gender (B) Mean difference (A−B) Standard error Significance** 95% confidence Interval for difference** Lower Upper Demirjian F M 0.05 0.11 0.626 −0.16 0.29 Willems F M −0.29* 0.10 0.005 −0.497 −0.09 Method Gender (A) Gender (B) Mean difference (A−B) Standard error Significance** 95% confidence Interval for difference** Lower Upper Demirjian F M 0.05 0.11 0.626 −0.16 0.29 Willems F M −0.29* 0.10 0.005 −0.497 −0.09 *The mean difference is significant at the 0.05 level. **Adjustment for multiple comparisons: Šidak. View Large Table 5. Comparison of the DA estimation by Demirijan and Willems methods within gender [female (F) and male (M)]. (Based on estimated marginal means.) Gender Method (A) Method (B) Mean difference (A−B) Standard error Significance** 95% confidence interval for difference** Lower Upper F Demirjian Willems 0.81* 0.03 0.000 0.74 0.88 M Demirjian Willems 0.46* 0.03 0.000 0.41 0.52 Gender Method (A) Method (B) Mean difference (A−B) Standard error Significance** 95% confidence interval for difference** Lower Upper F Demirjian Willems 0.81* 0.03 0.000 0.74 0.88 M Demirjian Willems 0.46* 0.03 0.000 0.41 0.52 *The mean difference is significant at the 0.05 level. **Adjustment for multiple comparisons: Šidak. View Large DA methods compared to the CA Both methods overestimated the difference between DA and CA. The percentage of children that were estimated equal or less than 1 year from their CA was higher in Willems method compared to Demirjian’s in the two groups and subgroup categories (Table 2). The correlation of Demirjian’s DA/CA difference was weak (r = 0.372) and comparable to the Willems method (Figure 4). A statistical difference was observed between DA deviating from CA, depending on which method is used (P = 0.000) (Table 5). Figure 5 shows the distribution of Demirjian and Willems DA estimation to the CA. Figure 4. View largeDownload slide Pearson’s reliability of Demirjian and Willems DA estimation methods to the CA of the sample. Figure 4. View largeDownload slide Pearson’s reliability of Demirjian and Willems DA estimation methods to the CA of the sample. Figure 5. View largeDownload slide Distribution of Demirjian and Willems DA estimation methods as compared to the CA of the sample. Figure 5. View largeDownload slide Distribution of Demirjian and Willems DA estimation methods as compared to the CA of the sample. Discussion The aim of this study was to determine and compare the Demirjian and Willems DA/CA difference in children with NSUCLP and a control group. The study also used the data to explore differences between the methods, gender, and groups. The NSCULP group had a larger population of boys than girls and the cleft defect was predominantly left sided. This cohort is representative of the natural distribution of this congenital defect in Sweden (23, 39). While not intentional, the inter-examiner reliability was found to be good and this too was in accordance with previous results (13, 14, 31, 32). Demirjian et al. (8) devised their method based on their study of French/Canadian children. Yet the method inconsistently overestimates DA when it is applied to children of other ethnicities (10–14). A number of possible explanations may account for this methodological weakness. First, growth patterns vary with ethnicity, limiting the validity of the method for different ethnic groups (33). Second, the individual plots and tables used to compile the original linear regression graphs were combined and manually smoothed, resulting in an inherent weakness in the graph (34). Erroneous application of the method, inappropriate statistical methodology, and small sample size may also contribute to the overestimation effect (35). To the best of our knowledge, this is the first study in which Demirjian and Willems methods for calculating DA in CL/P children have been directly compared. The literature indicates that compared to people without the defect, the development of maxillary and mandibular teeth is often delayed in patients with CL/P (24, 25, 28, 36). There is a correlation between the extent of the delay with the severity and type of cleft; where the cleft occurs only in the lip, the delay is less than when the palate is involved (37, 38). The reasons for delayed development have yet to be elucidated; however, it is possible that it could be attributed to the genetic predisposition of the cleft. The DA for both groups was overestimated by the methods used in this study. According to the Demirjian method, the DA for NSUCLP group was 0.83 ± 0.73 years, whereas the control group was 1.08 years ± 0.84, making the average difference between the groups 0.25 years. Delayed DA phenomenon of CL/P patients has been observed to varying degree by diverse studies. A delay of 0.7 years was found by Bindayel et al. (39) in a combined sample of unilateral and bilateral CL/P Saudi patients. Tan et al. (40) investigated a sample of UCLP patients from Singapore and found a DA delay of 0.55 years. Lai et al. (41) found there to be less of a delay (0.37 years) in their study of Chinese patients with various cleft types. The difference of NSUCLP and control groups DA/CA difference by using the Willems method was 0.27 years, which is comparable to that found by the Demirjian method; however the Willems method was more accurate in its DA estimations than the Demirjian method (0.22 ± 0.68 years for NSUCLP and 0.47 ± 0.83 years for the control group). The literature has failed to yield a study to compare these results for the Willems method for children with NSUCLP, but the superior accuracy of this method over Demirjians or other techniques is recognized in the literature (2, 15–17). The positive secular trend has been proposed in literature to explain the overestimation of older DA methods, yet in counter of this argument, there is a lack of evidence to support a secular trend on the duration and timing of tooth formation (3). Whatever the reason for delayed DA, the Willems method predicts DA and relates CA more accurately in the NSUCLP children than in the control group. The implication is that the Willems method can be used to estimate the DA of Caucasian children with NSUCLP with reasonable accuracy. Both methods showed weakness in estimating the correlation between CA and DA. This reflects the findings of Hägg and Matsson’s study, which showed the Pearson correlation between the two variables to be weak, r = 0.3–0.6 in children aged between 6.5 and 12.5 years (42). Our observation agrees with that of Huyskens et al. (43), in which the higher the CA of a child, there was greater variability arising from the Willems and Demirjian methods incorrectly estimating ages. The rationale for this finding can be explained by younger patients having more teeth in development, giving more points to estimate DA; this advantage flattens in children aged more than 10 years, indicating that DA estimation will be most accurate in children below this age. Compared to pre-adolescent (8–13 years) boys, girls’ somatic growth and dental development is more advanced (44). The same pattern is also present in estimated DA, with girls being more advanced than boys, except for their third molars, which develop later than in boys (45). The Demirjian method does not always reflect this and the literature presents contradictory findings for gender differences. Various researchers have found Demirjian’s method overestimates more for girls than boys (11, 14, 16, 31), while others have not (2, 46). Using the Demirjian method in this study, we found girls to be more advanced than boys, but this finding was not statistically significant. The opposite trend was observed using the Willems method, which extended to within group gender evaluations, with boys advancing before girls. The prime limitation of this study was the unbalanced distribution of gender among the two groups. Regardless that NSUCLP and healthy children were consecutively selected, this reflects Sweden’s real distribution pattern of this birth defect (23). For this reason, we adjusted for the effect of gender on the outcome (Table 3). A further limitation of the study is that the sample was restricted to children aged approximately 9–11 years; however, this corresponds to the age at which most Swedish children with CLP have panoramic radiographs taken in preparation for bone grafting procedures. Future work could explore other age groups to collect broader data from younger and older patients. Lebbe et al. (47) found that there is a greater delay in dental development in patients with tooth agenesis compared to those without agenesis and that there was a positive correlation between the length of the delay and the number of teeth congenitally absent. As such, another strand of future work could investigate dental development in NSUCLP children with agenesis and compare against NSUCLP children without agenesis. Conclusion The null hypothesis was rejected. Willems’ method as compared to Demirjian’s displayed greater accuracy in estimating CA. Both methods overestimated CA but detected DA delay in NSUCLP children compared to the control group. However, the methods were inconsistent in estimating CA within gender. Conflict of interest Authors declare that there is no conflict of interest. Acknowledgement We are greatly thankful to E. Hagel for her sincere statistical assistance and for the Saudi Arabian Cultural office in Berlin for their support to the main author. References 1. Green L. J. ( 1961) The interrelationships among height, weight and chronological, dental and skeletal ages. Angle Orthodontist , 31, 189– 193. 2. Maber M. Liversidge H. M. and Hector M. P. ( 2006) Accuracy of age estimation of radiographic methods using developing teeth. Forensic Science International , 159, S68– S73. Google Scholar CrossRef Search ADS PubMed 3. Liversidge H. M. ( 2015) Controversies in age estimation from developing teeth. Annals of Human Biology , 42, 397– 406. Google Scholar CrossRef Search ADS PubMed 4. Cunha E.et al. ( 2009) The problem of aging human remains and living individuals: a review. Forensic Science International , 193, 1– 13. Google Scholar CrossRef Search ADS PubMed 5. Bean R. B. ( 1914) Eruption of teeth as physiological standard for testing development. Pedagogical Seminary , 21, 596– 614. Google Scholar CrossRef Search ADS 6. Cattell P. ( 1928) Dentition as Measure of Maturity . Harvard University Press, Cambridge, USA. 7. Suri L. Gagari E. and Vastardis H. ( 2004) Delayed tooth eruption: pathogenesis, diagnosis, and treatment. a literature review. American Journal of Orthodontics and Dentofacial Orthopedics , 126, 432– 445. Google Scholar CrossRef Search ADS PubMed 8. Demirjian A. Goldstein H. and Tanner J. M. ( 1973) A new system of dental age assessment. Human Biology , 45, 211– 227. Google Scholar PubMed 9. Fanning E. A. ( 1961) A longitudinal study of tooth formation and root resorption. The New Zealand Dental Journal , 57, 202– 217. 10. Mörnstad H. Reventlid M. and Teivens A. ( 1995) The validity of four methods for age determination by teeth in Swedish children: a multicentre study. Swedish Dental Journal , 19, 121– 130. Google Scholar PubMed 11. Willems G. Van Olmen A. Spiessens B. and Carels C. ( 2001) Dental age estimation in Belgian children: Demirjian’s technique revisited. Journal of Forensic Sciences , 46, 893– 895. Google Scholar CrossRef Search ADS PubMed 12. Leurs I. H. Wattel E. Aartman I. H. Etty E. and Prahl-Andersen B. ( 2005) Dental age in Dutch children. European Journal of Orthodontics , 27, 309– 314. Google Scholar CrossRef Search ADS PubMed 13. Maia M. C. Martins Mda G. Germano F. A. Brandão Neto J. and da Silva C. A. ( 2010) Demirjian’s system for estimating the dental age of northeastern Brazilian children. Forensic Science International , 200, 177.e1– 177.e4. Google Scholar CrossRef Search ADS 14. Kırzıoğlu Z. and Ceyhan D. ( 2012) Accuracy of different dental age estimation methods on Turkish children. Forensic Science International , 216, 61– 67. Google Scholar CrossRef Search ADS PubMed 15. Liversidge H. M. Smith B. H. and Maber M. ( 2010) Bias and accuracy of age estimation using developing teeth in 946 children. American Journal of Physical Anthropology , 143, 545– 554. Google Scholar CrossRef Search ADS PubMed 16. Lee S. S.et al. . ( 2011) Validity of Demirjian’s and modified Demirjian’s methods in age estimation for Korean juveniles and adolescents. Forensic Science International , 211, 41– 46. Google Scholar CrossRef Search ADS PubMed 17. Grover S. Marya C. M. Avinash J. and Pruthi N. ( 2012) Estimation of dental age and its comparison with chronological age: accuracy of two radiographic methods. Medicine , Science , and the Law , 52, 32– 35. 18. Kirschner R. E. and LaRossa D. ( 2000) Cleft lip and palate. Otolaryngologic Clinics of North America , 33, 1191– 1215. Google Scholar CrossRef Search ADS PubMed 19. Goodacre T. and Swan M. C. ( 2008) Cleft lip and palate: current management. Paediatric Forensic Medicine and Pathology , 18, 283– 292. 20. Crockett D. J. and Goudy S. L. ( 2014) Cleft lip and palate. Facial Plastic Surgery Clinics of North America , 22, 573– 586. Google Scholar CrossRef Search ADS PubMed 21. Fogh-Andersen P. ( 1967) Genetic and non-genetic factors in the etiology of facial clefts. Scandinavian Journal of Plastic and Reconstructive Surgery , 1, 22– 29. Google Scholar CrossRef Search ADS 22. Dixon M. J. Marazita M. L. Beaty T. H. and Murray J. C. ( 2011) Cleft lip and palate: understanding genetic and environmental influences. Nature Reviews Genetics , 12, 167– 178. Google Scholar CrossRef Search ADS PubMed 23. Hagberg C. Larson O. and Milerad J. ( 1998) Incidence of cleft lip and palate and risks of additional malformations. Cleft Palate Craniofacial Journal , 35, 40– 45. Google Scholar CrossRef Search ADS PubMed 24. Ranta R. ( 1986) A review of tooth formation in children with cleft lip/palate. American Journal of Orthodontics and Dentofacial Orthopedics , 90, 11– 18. Google Scholar CrossRef Search ADS PubMed 25. Harris E. F. and Hullings J. G. ( 1990) Delayed dental development in children with isolated cleft lip and palate. Archives of Oral Biology , 35, 469– 473. Google Scholar CrossRef Search ADS PubMed 26. Akcam M. O. Evirgen S. Uslu O. and Memikoğlu U. T. ( 2010) Dental anomalies in individuals with cleft lip and/or palate. European Journal of Orthodontics , 32, 207– 213. Google Scholar CrossRef Search ADS PubMed 27. Pegelow M. Alqadi N. and Karsten A. L. ( 2012) The prevalence of various dental characteristics in the primary and mixed dentition in patients born with non-syndromic unilateral cleft lip with or without cleft palate. European Journal of Orthodontics , 34, 561– 570. Google Scholar CrossRef Search ADS PubMed 28. Brouwers H. J. and Kuijpers-Jagtman A. M. ( 1991) Development of permanent tooth length in patients with unilateral cleft lip and palate. American Journal of Orthodontics and Dentofacial Orthopedics , 99, 543– 549. Google Scholar CrossRef Search ADS PubMed 29. Calculate age in decimal years . http://www.ucsdbglab.org/tools/age.asp ( 12 February 2017, date last accessed). 30. Cicchetti D. V. ( 1994) Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instrument in psychology. Psychological Assessment , 6, 284– 290. Google Scholar CrossRef Search ADS 31. Nykänen R. Espeland L. Kvaal S. I. and Krogstad O. ( 1998) Validity of the Demirjian method for dental age estimation when applied to Norwegian children. Acta Odontologica Scandinavica , 56, 238– 244. Google Scholar CrossRef Search ADS PubMed 32. Lee S. E. Lee S. H. Lee J. Y. Park H. K. and Kim Y. K. ( 2008) Age estimation of Korean children based on dental maturity. Forensic Science International , 178, 125– 131. Google Scholar CrossRef Search ADS PubMed 33. Proffit W. R. FieldsJr H. W. and Sarver D. M. ( 2013) Contemporary Orthodontics . Elsevier Health Sciences, St Louis, USA. 34. Teivens A. and Mörnstad H. ( 2001) A modification of the Demirjian method for age estimation in children. Journal of Forensic OdontoStomatology , 19, 26– 30. 35. Liversidge H. M. and Molleson T. I. ( 1999) Developing permanent tooth length as an estimate of age. Journal of Forensic Sciences , 44, 917– 920. Google Scholar CrossRef Search ADS PubMed 36. Ranta R. ( 1972) A comparative study of tooth fomation in the permanent dentition of Finnish children with cleft lip and palate. an orthopantomographic study. Proceedings of the Finnish Dental Society , 68, 58– 66. 37. Pöyry M. Nyström M. and Ranta R. ( 1989) Tooth development in children with cleft lip and palate: a longitudinal study from birth to adolescence. European Journal of Orthodontics , 11, 125– 130. Google Scholar CrossRef Search ADS PubMed 38. Hazza’a A. M. Rawashdeh M. A. Al-Jamal G. and Al-Nimri K. S. ( 2009) Dental development in children with cleft lip and palate: a comparison between unilateral and bilateral clefts. European Journal of Paediatric Dentistry , 10, 90– 94. Google Scholar CrossRef Search ADS PubMed 39. Bindayel N. A. AlSultan M. A. and ElHayek S. O. ( 2014) Timing of dental development in Saudi cleft lip and palate patients. Saudi Medical Journal , 35, 204– 308. 40. Tan E. L. Yow M. Kuek M. C. and Wong H. C. ( 2012) Dental maturation of unilateral cleft lip and palate. Annals of Maxillofacial Surgery , 2, 158– 162. Google Scholar CrossRef Search ADS PubMed 41. Lai M. C. King N. M. and Wong H. M. ( 2008) Dental development of Chinese children with cleft lip and palate. Cleft Palate–Craniofacial Journal , 45, 289– 296. Google Scholar CrossRef Search ADS PubMed 42. Hägg U. and Matsson L. ( 1985) Dental maturity as an indicator of chronological age: the accuracy and precision of three methods. European Journal of Orthodontics , 7, 25– 34. Google Scholar CrossRef Search ADS PubMed 43. Huyskens R. W. Katsaros C. Van ‘t Hof M. A. and Kuijpers-Jagtman A. M. ( 2006) Dental age in children with a complete unilateral cleft lip and palate. Cleft Palate Craniofacial Journal , 43, 612– 615. Google Scholar CrossRef Search ADS PubMed 44. Helm S. ( 1969) Secular trend in tooth eruption: a comparative study of Danish school children of 1913 and 1965. Archives of Oral Biology , 14, 1177– 1191. Google Scholar CrossRef Search ADS PubMed 45. Kullman L. Johanson G. and Akesson L. ( 1992) Root development of the lower third molar and its relation to chronological age. Swedish Dental Journal , 16, 161– 167. Google Scholar PubMed 46. Ambarkova V. Galić I. Vodanović M. Biočina-Lukenda D. and Brkić H. ( 2014). Dental age estimation using Demirjian and Willems methods: cross-sectional study on children from the Former Yugoslav Republic of Macedonia. Forensic Science International , 234, 187.e1– 187.e7. Google Scholar CrossRef Search ADS 47. Lebbe A. Cadenas de Llano-Pérula M. Thevissen P. Verdonck A. Fieuws S. and Willems G. ( 2017) Dental development in patients with agenesis. International Journal of Legal Medicine , 131, 537– 546. Google Scholar CrossRef Search ADS PubMed © The Author(s) 2017. Published by Oxford University Press on behalf of the European Orthodontic Society. All rights reserved. For permissions, please email: email@example.com
The European Journal of Orthodontics – Oxford University Press
Published: Feb 1, 2018
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera