Reliability of London Atlas for Dental Age Estimation in an Australian Cohort

Reliability of London Atlas for Dental Age Estimation in an Australian Cohort | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Reliability of London Atlas for Dental Age Estimation in an Australian Cohort Amanda Chua, Christabel Halim, Ethan Pham, Selwin Samuel, Sobia Zafar, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6236330/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction Age estimation is a very essential tool that is required for quite a lot of purposes in legal settings and for disaster victim identification purposes. In the recent years, London Atlas for dental age estimation has gained popularity and its applicability in Australia has not been sufficiently validated. Therefore, a study was aimed to study the ability of London Atlas to accurately estimate the ages of an Australian cohort. Aim To test the age prediction reliability of the London Atlas on an Australian population and to determine if there is a difference in its prediction accuracy between males and females. Methods A total of 193 panoramic radiographs were accessed from the University of Queensland’s archival records. The London Atlas was used to estimate the dental ages of these radiographs of 96 females and 97 males, aged between 5 and 17 years. Results Of the entire cohort, the difference between the mean estimated age (11.56 years) and mean chronological (11.92 years) age was 0.36 years. This difference was found to be statistically significant (p < 0.001). The over-estimation of ages was significant in age groups 6, 7, 8,10 and 11. The mean age difference for males was 0.038 years while the difference for females was 0.471 years. However, the difference between the two sexes (0.509 years) was statistically insignificant (p > 0.001). The London Atlas shows a tendency to over-estimate ages of females and under-estimate ages of males. Conclusion The London Atlas was found to overestimate the ages of children in an Australian population by approximately four and a half months (0.37 years). However, there was no difference in age prediction accuracy between males and females. Overall, the London Atlas has comparable accuracy with other dental age estimation methods and should be considered as a tool for age estimation. Forensic Medicine Dentistry Anthropology London Atlas Dental age estimation Australian population Panoramic radiographs Figures Figure 1 Figure 2 Figure 3 INTRODUCTION The age of an individual is a critical characteristic that has far-reaching implications on the society ( 1 ). Age determines a person’s eligibility to attend school, seek work, and participate in various social activities ( 1 – 2 ). It also determines how the justice system handles cases of child labour, child abuse, child marriage, adoption, kidnapping and criminal cases involving minors ( 2 – 3 ). Age estimation is necessary as it helps to identify unknown or missing persons; identify casualties in large-scale disasters; expedite the process of determining the age of illegal migrants or migrants lacking valid documentation ( 2 ). The medico legal consequences of unidentified individuals are dire, hence, methods for age estimation are crucial to determine the age of individuals in such cases ( 2 ). Several methods have been used for age estimation, such as radiological examination of dental development, radiological examination of skeletal development and aspartic acid racemization in dentine ( 4 – 7 ), to name a few. It has been shown that tooth development and eruption patterns are more consistent with the age of individuals as they are less prone to being affected by environmental and nutritional factors ( 5 – 6 ). Additionally, while some studies show that age estimation using aspartic acid racemization in dentine produce more precise results, this method requires extracted teeth which is not ideal in the case of living individuals ( 6 ). Hence, knowing the dental age and timing of dental development via radiographs is quite customary in our current society, and has the potential to be the most practical and reliable tool for age estimation as it is non-invasive and more practical in living individuals. Tooth development stages can be assessed via tooth specific methods or through dental charts (5). Tooth specific methods predict age from a specific developmental stage of a single tooth, which can be influenced by the age distribution and age range of the test sample. Therefore, an analysis by age categories via dental charts which cover the whole dentition, can provide a more reliable information 5 . One of the oldest and well-known dental age estimation charts is the atlas of Schour and Massler (1941), which consist of 21 drawings of human dentition between the age of five months and 35 years ( 8 ). However, the lack of drawings for ages 12 to 15 and 15 to 21, gives unreliable results for older subjects. Ubelaker’s dental chart (1978) was an attempt to improve the atlas of Schour and Massler (1941) ( 8 – 9 ). Ubelaker utilised published sources to correct the age range for each drawing and covered more variations for each age range ( 10 ). This chart has also been modified for use in Australia, with separate charts for males and females ( 10 ). Several other similar charts were also developed as a result, such as Gustafson and Koch (1974), Brown (1985), Kahl and Schwarze (1988) ( 5 ). However, none of these charts proved more superior than the other ( 5 ). Other proven radiographic age estimation techniques involve a scoring system such as Demirjian’s and Willem’s methods ( 11 – 12 ). These methods involve assignment of scores to the lower left-hand side permanent teeth. These scores are combined to calculate a maturity score and dental age ( 13 ). The London Atlas was developed as an evidence-based atlas to assess dentition for age estimation. It is comprehensive, consisting of 31 age categories, using both tooth development and alveolar eruption of individuals ( 5 ). Tooth development and eruption is illustrated in this atlas for ages 1 to 23, with diagrams at the midpoint of each chronological year ( 5 ). “The London Atlas” has emerged as one of the more accurate and reliable methods of age estimation in the British Bangladeshi and Hispanic population ( 4 – 5 ). However, this has not yet been tested in an Australian population. This study will test the accuracy and reliability of “The London Atlas” as an age estimation tool in an Australian population. AIMS AND HYPOTHESES The aim of this study is to test the reliability of The London Atlas in an Australian population. Two hypotheses will be tested: there will be a difference between the chronological age and the age predicted by the London Atlas, there will be a difference in the prediction accuracy of the London Atlas between males and females. MATERIALS AND METHODS This was a retrospective cross-sectional study of orthopantomograms (OPG) of individuals between the ages 5 and 17 years. Dental records of patients, who consented for their information to be used for research purposes, were included in this study. Ethics approval Ethics approval for this study was reviewed by Human Research Ethics Committee at The University of Queensland (UQ) via the low-risk review pathway. The risk of breach of privacy was controlled via de-identification and minimized as only examiners (S.Z and S.A) had access to the identified records. Sample selection The total number of radiographs obtained from the university’s archives was 600. This sample size (n = 193) was calculated using a single mean estimation with standard deviation (SD) of 14 units in maturity score and precision of 5 units. Selection process Good quality OPGs were selected (n = 193) with all teeth in focus, of healthy individuals aged 5–17 years from the archival records of the University of Queensland (UQ), School of Dentistry. Of the 193 radiographs, 49.7% were male and 50.3% were female. Excluded samples comprised of unclear radiographs, patients with hypodontia, hyperdontia, gross anomalies and pathologies (eg. Taurodontism, microdontia, Amelogenesis Imperfecta, Dentinogenesis Imperfecta, tumours, abscesses, cysts, fractures etc.), presence of gross caries, previous orthodontic treatment or severe malocclusion. De-identification of radiographs The dental record archives were reviewed by the two examiners (S.Z and S.A). Radiographs were selected based on the inclusion and exclusion criteria of the study as given in Fig. 1. Subjects who satisfied the criteria had their gender, date of birth and date of the radiograph collected. This data was de-identified and assigned a study identification number to maintain patient confidentiality. This study identification number is generated by a random number generator (RANDOM.ORG). The examiners maintained a separate link between the study identification and the patient’s electronic health record number. The de-identified radiographs were randomly assigned to each assessor. Age estimation using the London Atlas The de-identified radiographs were assessed by other investigators of the study (n = 3) to determine the developmental and eruption stages of all teeth on the left side, in both upper and lower jaws, according to the London atlas ( 12 ). Stage of development and eruption of each tooth was entered in the template table (Fig. 1). The estimated age was generated using the Lond Atlas Software (Queen Mary Innovation Ltd, London, United Kingdom) (Fig. 2 ). The chronological age (real age) for each subject will be calculated by subtracting the date of the radiograph from the date of birth after having converted both to a decimal age using Eveleth and Tanner’s method ( 11 ). The chronological age was blinded from the evaluators until after the age estimation was completed for all radiographs. Analysis of data The chronological age was subtracted from the estimated age, a positive result suggests an overestimation in age while a negative result suggests an underestimation. The mean difference of chronological age and estimated age of the whole sample was calculated. The mean difference for each age category was also calculated. A paired sample t-test was used to determine if the mean difference is significant. The mean difference in chronological age and estimated age for males and females were compared with the independent sample t-test. This will assess the accuracy of the London Atlas in age estimation between males and females. For all analyses, a p-value of less than 0.05 was considered a statically significant difference. Inter-examiner and Intra-examiner reliability Before the data collection process, assessors underwent an inter- and intra-examiner reliability test. A sample of 10 radiographs were assessed by each assessor. The same sample was also assessed by an examiner. Cohen’s kappa coefficient will be calculated to compare each of the student’s results against the examiner’s results for inter-examiner reliability. The radiographic sample was then reassessed by the assessors after an interval of 2 weeks to evaluate intra-examiner reliability, also using Cohen’s kappa. RESULTS Of the 600 radiographs, only 193 satisfied the inclusion criteria, where 96 radiographs were of female subjects and 97 radiographs were of male subjects. Pre-data collection, inter-examiner and intra-examiner tests yielded Cohen’s Kappa scores of 0.89 and 0.81 respectively (Table 1 ). Table 1 Inter and Intra-examiner kappa values Raters Inter-examiner Reliability Intra-examiner Reliability Rater 1 0.880 0.840 Rater 2 0.900 0.800 Rater 3 0.890 0.800 The mean estimated age for the collected sample was 11.56 years whilst the mean chronological age was 11.92 years. The mean difference of + 0.36 (standard deviation = 1.67) was found to be significant (p = 0.003). The overestimation of ages in age groups 6,7, 8, 10 and 11 were statistically significant, but were insignificant in age groups 9, 12, 13 and 15. The two largest overestimations (1.11 years and 1.05 years respectively) were found in age group 10 and 11.The atlas underestimated ages in groups 14 and 16, with the largest underestimation of 0.98 years in the 14-14.99 years age group, but the mean difference was statistically insignificant. Overall, the London Atlas was found to significantly over-estimate ages of the Australian children. Table 2 Mean difference between chronological and estimated age by age groups Age group (years) No. Mean difference SD* Sig. (p < 0.05) SE* 95% Confidence Interval of the Difference t-score Lower Upper All samples 193 0.362 1.672 0.003 0.122 0.119 0.608 2.990 6-6.99 11 0.578 0.553 0.006 0.162 0.206 0.949 3.461 7-7.99 19 0.308 0.615 0.043 0.135 0.114 0.604 2.182 8-8.99 21 0.716 1.022 0.004 0.224 0.250 1.181 3.209 9-9.99 20 0.339 1.437 0.305 0.308 -0.334 1.011 1.054 10-10.99 12 1.112 1.149 0.006 0.325 0.382 1.842 3.352 11-11.99 20 1.050 1.962 0.027 0.426 0.132 1.968 2.393 12-12.99 19 0.039 1.391 0.796 0.307 -0.587 0.754 0.263 13-13.99 17 0.685 2.004 0.178 0.473 -0.346 1.715 1.409 14-14.99 18 -0.980 2.132 0.848 0.488 -1.158 0.962 -0.195 15-15.99 19 0.107 2.457 0.852 0.544 -1.077 1.292 0.190 16-16.99 15 -0.842 1.675 0.072 0.412 -1.770 0.853 -1.948 *SD – Standard deviation; SE – Standard error Table 3 summarises the mean age difference (difference between chronological and estimated age) in males and females where that of males was − 0.038, while that of females was 0.471, showing that the London Atlas overestimated ages of female subjects and underestimated ages of male subjects. The results for males and females were compared using an independent samples t-test (Table 4 ). The age difference between males and females is 0.509. However, the difference in variance and difference between the two genders were not significant (p = 0.575 and p = 0.110 respectively). Therefore, there was no significant difference in the accuracy of the London Atlas in estimating ages of males or females. Table 3 Estimated and chronological age difference in males and females Sex Females Males Mean 0.471 -0.038 Standard deviation 2.560 1.772 Minimum -15.550 -4.600 Maximum 4.550 7.080 Table 4. Difference between estimated and chronological ages of male and female subjects Independent samples test Levene’s test for equality of variances t-test for equality of means F* sig* t* df* Sig* Mean diff SE diff* 95% CI* Lower Upper Difference: males vs females Equal variances assumed 0.316 0.575 1.607 191.000 0.110 0.509 0.317 -0.116 1.133 Equal variances not assumed 1.604 168.889 0.111 0.509 0.317 -0.118 1.135 *F – Fisher ratio; sig — significance; t— t-value; df—degrees of freedom; SE diff — standard error difference; 95% CI — 95% confidence interval DISCUSSION Dental development has shown to be one of the more uniform processes in humans and is commonly associated with the actual age of an individual. Dental radiographs are often used to estimate dental age as it can be used in living individuals, is economical, easy to access and reliable ( 14 ). This study investigated the reliability of the London Atlas in age estimation of the Australian population. This was carried out by measuring the difference in chronological and estimated age of the individual, which indicated either an overestimation or underestimation in age. Using the London Atlas, there was an underestimation in the age for males and overestimation in the age for females. This supports studies using Demirjian’s methods, where the overestimation and underestimation of ages were explained by slight discrepancies in the dental development between males and females ( 11 , 15 ). However, there was no significant difference in the accuracy of the London Atlas in estimating ages of males or females, and this coincides with results obtained from the study of the London Atlas in the Hispanic and Thai population ( 16 – 17 ). The London Atlas was generally found to overestimate the ages of children in the Australian population. This was an average overestimation of 0.36 years, which equates to approximately four and a half months. This difference of four and a half months of age would not result in significant undesirable consequences, when applied to real world circumstances. The overestimation bias was statistically significant in several of the younger age groups, indicating that the atlas may be more inaccurate and overestimates more so in the younger population of Australian children. This slight overestimation was consistent with the results obtained when the London Atlas was applied to the Thai population ( 17 ). Furthermore, in this study, children aged 10 and 11 had the largest over estimation (1.11 years and 1.05 years respectively). Recent studies on the Hispanic and Brazilian children population gave similar results. When the London Atlas was applied, Hispanic children aged 11 to 12 and Brazilian children aged 11 had one of the largest overestimations ( 16 , 18 ). The average overestimation of 0.36 years from this study is consistent with the study conducted in Hispanic children using The London Atlas, where an average overestimation of 0.35 years was obtained ( 16 ). This result also concurs with the findings of the London Atlas in the Portuguese population, although the overestimated amount was one month, which was lesser than that in this study ( 19 ). Additionally, The London Atlas during this study proved similar estimation to Demirjian’s methods previously tested in various Australian populations ( 11 – 12 ). Flood et al. used Demirjian’s methods including more recent modified methods and found average age overestimation ranging from 0.31 to 0.61 years in a South Australian population ( 12 ). This method is usually found to be more time consuming due to more in depth analysis of each individual tooth. This study utilised an almost equal distribution of males (n = 97) and females (n = 96). Additionally, a significant number of radiographs were initially obtained to ensure a higher probability of members from the population to be included in the dataset. These radiographs were also filtered according to a strict inclusion and exclusion criteria, which produced 193 suitable radiographs. This study had several limitations. While the radiographs had strict inclusion and exclusion criteria, the quality of the radiographs could have varied. These images were taken on different machines in the facility and taken by different personnel. This non-standardised quality could have affected the interpretation of the developmental stages. The London atlas age estimation method could also be limited by the variation in the development of third molars. However, to this date, it is debatable whether the inclusion of third molars in age estimation affects the accuracy of the results ( 20 ). It is evident that third molars vary in development, morphology and positioning, and could potentially introduce more error in predicting one’s age ( 20 ). Several studies even proposed that the rate of third molars developing, differs across different countries and ethnic groups ( 20 – 21 ). However, these studies concluded that the small differences in third molar development had little impact on age estimations due to the large standard deviation of developmental stages in each age group ( 20 – 21 ). Additionally, this population-specific study targeting solely the Australian population, reduced the error of possible cross-population variability. The study of the London Atlas for age estimation in Portugese population also concluded that third molars should be used in age estimation as the results yielded are as reliable as using a whole set of teeth ( 19 ). Another limitation of the London atlas is that it estimates the age to the precision of the midpoint of each year (e.g 6.5 years), whereas other studies including Demirijian’s methods, produces point estimates of age. However, the London Atlas has proven to be superior in accuracy when compared with most point estimate methods ( 5 , 11 ). The difference between the dental age and chronological age might also not be attributed to the shortcomings of the respective dental age estimation methods. Similar to other biological processes of the human species, dental development also has a certain amount of variation ( 14 , 16 ). Therefore, this difference might not be a limitation of the respective age estimation method but a limitation to using dental age as an estimation method ( 16 ). It is likely that technologies like machine learning and artificial intelligence (AI) will play a larger role in forensic age estimation moving forward, as other studies also suggest ( 22 ). However, we believe it is crucial to first grasp the foundational principles of this area of research before relying on advanced technologies, so as to better interpret the outcomes of automated methods. The London Atlas is emerging amongst many other age estimation methods. It is easily accessible in various languages and has comprehensive illustrations of various developmental stages and age categories. The results of the present study are comparable with other studies and also concludes The London Atlas as a reliable tool for age estimation. The London Atlas will need to be performed in different countries or ethnic groups to assess its robustness in age estimation across populations. One way of doing this would be to do a targeted study on Australian Aboriginals and Torres Strait Islanders versus Caucasoids. Reports have shown that Australian Aboriginals and Torres Strait Islanders have faster dental development than those of European descent ( 23 ). A comparison of the London Atlas and another age estimation method on the specific target group would be beneficial in determining which method is more accurate. Furthermore, obtaining a large enough sample was a difficulty this study faced. This could be made possible in the future by utilising radiographs from multiple Queensland Health facilities. CONCLUSION Age estimation has significant relevance in current society. It guides decisions regarding child rights, justice, medico-legal disputes, migration and forensic science. The London Atlas is an emerging age estimation tool that is easily accessible, reproducible when used, and comprehensible. In this study, The London Atlas generally marginally overestimated the ages of Australian children and is equally accurate in estimating the age of males and females. Nevertheless, all current age estimation methods have shown its shortcomings. Overall, the London Atlas has comparable accuracy with other dental age estimation methods and should be considered as an adjunct tool for age estimation. Declarations DISCLOSURE STATEMENT The authors declare that they have no conflicts of interest to disclose. This research did not receive any financial support or funding from any external sources. All data generated or analysed during this study are included in the manuscript or are available from the corresponding author upon reasonable request. We confirm that all participants involved in this study provided informed consent to participate. In cases where participants were minors, consent was obtained from their legal guardians. Participants were fully informed about the study's purpose, procedures, potential risks, and benefits before their inclusion. This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Ethical approval for this study was obtained from the University of Queensland Human Research Ethics Committee, and all procedures were performed in accordance with the ethical standards of the Declaration of Helsinki. AUTHOR CONTRIBUTIONS SZ and SA contributed to the study conception and design. Material preparation, data collection and analysis were performed by AC, SS, CH and EP. The first draft of the manuscript was written by AC and SA. The manuscript was later edited and reviewed by SS. All authors read and approved the final manuscript. References Berndt DC, Despotovic T, Mund MT, Filippi A (2008) The role of dentist in modern forensic age determination. Schw Monats Zahnm 118:1073–1088 Lewis JM, Senn DR (2015) Forensic dental age estimation: an overview. J Calif Dent Assoc 43(6):315–319. https://doi.org/10.1080/19424396.2015.12222856 Manjunatha BS, Soni NK (2014) Estimation of age from development and eruption of teeth. 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Morphologie 108(360):100723. https://doi.org/10.1016/j.morpho.2023.100723 Brown T (1978) Tooth emergence in Australian aboriginals. Ann Hum Biol 5(1):41–54 Moorrees CF, Fanning EA, Hunt EE Jr (1963) Age variation of formation stages for ten permanent teeth. J Dent Res 42:490–502 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6236330","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":429366274,"identity":"7b7392f0-c46b-4315-9294-4413bd1bfcdc","order_by":0,"name":"Amanda Chua","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Amanda","middleName":"","lastName":"Chua","suffix":""},{"id":429366401,"identity":"9cabd038-6ff0-47e0-b1f6-21d630a9fb7e","order_by":1,"name":"Christabel 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Samuel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYFAC5gaGBBj7QQWYMiCghRFJS8IZYrXAQWIbEVrk2xvbJB7uYLDnn3bG8EHivMOyDezN2yQYag7j1GJw5mCbROIZhsQZt3OMDRK3HTZu4DlWJsFwDI8WicS2G0D3JDDczjGTAGpJbJAAMhjYcGuRn/8QrMVe/naO+Y/EOUAt8m+AWv7h1sJwgxGshXED0BaGxAaQLTxmEoxt+PyS2P4jEeidjbfTiiUSjqUbt/GkFVsk9qXjdlj74cOGP9ts7OVuJ2/88KHGWraf/fDGGx++WeN2GARIwFmM4KhJIKQBGSDH7CgYBaNgFIwCMAAAS+JXPa4RzXUAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Selwin","middleName":"","lastName":"Samuel","suffix":""},{"id":429366404,"identity":"82502c7e-9bd0-47d3-9813-1f66e7cc6f20","order_by":4,"name":"Sobia Zafar","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sobia","middleName":"","lastName":"Zafar","suffix":""},{"id":429366405,"identity":"2caf7493-5d53-431f-87f7-a8ad68ea422b","order_by":5,"name":"Sakher 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tooth development and age\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6236330/v1/13ac6b780c2e1c35537674f9.png"},{"id":79100254,"identity":"881e81ad-7dbb-4a75-aeb2-460c9ec293f3","added_by":"auto","created_at":"2025-03-24 12:07:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":76484,"visible":true,"origin":"","legend":"\u003cp\u003eSoftware entry table: tooth developmental stage\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6236330/v1/138946e84e9a44c3cdf39b18.png"},{"id":79100255,"identity":"befac1f6-0f51-432a-a4ef-4f7c17b0e61e","added_by":"auto","created_at":"2025-03-24 12:07:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28716,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of the methodological approach\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6236330/v1/d57016c4220a5a2e50b79947.png"},{"id":79101682,"identity":"d790af52-c7dd-441d-a8ad-8105360a3ab0","added_by":"auto","created_at":"2025-03-24 12:23:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":830147,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6236330/v1/f09e66c4-f8c6-4df5-a382-217c5c68d388.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eReliability of London Atlas for Dental Age Estimation in an Australian Cohort\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe age of an individual is a critical characteristic that has far-reaching implications on the society (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Age determines a person\u0026rsquo;s eligibility to attend school, seek work, and participate in various social activities (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). It also determines how the justice system handles cases of child labour, child abuse, child marriage, adoption, kidnapping and criminal cases involving minors (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Age estimation is necessary as it helps to identify unknown or missing persons; identify casualties in large-scale disasters; expedite the process of determining the age of illegal migrants or migrants lacking valid documentation (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The medico legal consequences of unidentified individuals are dire, hence, methods for age estimation are crucial to determine the age of individuals in such cases (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral methods have been used for age estimation, such as radiological examination of dental development, radiological examination of skeletal development and aspartic acid racemization in dentine (\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), to name a few. It has been shown that tooth development and eruption patterns are more consistent with the age of individuals as they are less prone to being affected by environmental and nutritional factors (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Additionally, while some studies show that age estimation using aspartic acid racemization in dentine produce more precise results, this method requires extracted teeth which is not ideal in the case of living individuals (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Hence, knowing the dental age and timing of dental development via radiographs is quite customary in our current society, and has the potential to be the most practical and reliable tool for age estimation as it is non-invasive and more practical in living individuals.\u003c/p\u003e \u003cp\u003eTooth development stages can be assessed via tooth specific methods or through dental charts (5). Tooth specific methods predict age from a specific developmental stage of a single tooth, which can be influenced by the age distribution and age range of the test sample. Therefore, an analysis by age categories via dental charts which cover the whole dentition, can provide a more reliable information\u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOne of the oldest and well-known dental age estimation charts is the atlas of Schour and Massler (1941), which consist of 21 drawings of human dentition between the age of five months and 35 years (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). However, the lack of drawings for ages 12 to 15 and 15 to 21, gives unreliable results for older subjects. Ubelaker\u0026rsquo;s dental chart (1978) was an attempt to improve the atlas of Schour and Massler (1941) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Ubelaker utilised published sources to correct the age range for each drawing and covered more variations for each age range (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). This chart has also been modified for use in Australia, with separate charts for males and females (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Several other similar charts were also developed as a result, such as Gustafson and Koch (1974), Brown (1985), Kahl and Schwarze (1988) (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). However, none of these charts proved more superior than the other (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOther proven radiographic age estimation techniques involve a scoring system such as Demirjian\u0026rsquo;s and Willem\u0026rsquo;s methods (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). These methods involve assignment of scores to the lower left-hand side permanent teeth. These scores are combined to calculate a maturity score and dental age (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe London Atlas was developed as an evidence-based atlas to assess dentition for age estimation. It is comprehensive, consisting of 31 age categories, using both tooth development and alveolar eruption of individuals (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Tooth development and eruption is illustrated in this atlas for ages 1 to 23, with diagrams at the midpoint of each chronological year (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). \u0026ldquo;The London Atlas\u0026rdquo; has emerged as one of the more accurate and reliable methods of age estimation in the British Bangladeshi and Hispanic population (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). However, this has not yet been tested in an Australian population. This study will test the accuracy and reliability of \u0026ldquo;The London Atlas\u0026rdquo; as an age estimation tool in an Australian population.\u003c/p\u003e\n\u003ch3\u003eAIMS AND HYPOTHESES\u003c/h3\u003e\n\u003cp\u003eThe aim of this study is to test the reliability of The London Atlas in an Australian population. Two hypotheses will be tested:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ethere will be a difference between the chronological age and the age predicted by the London Atlas,\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ethere will be a difference in the prediction accuracy of the London Atlas between males and females.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003eThis was a retrospective cross-sectional study of orthopantomograms (OPG) of individuals between the ages 5 and 17 years. Dental records of patients, who consented for their information to be used for research purposes, were included in this study.\u003c/p\u003e\n\u003ch3\u003eEthics approval\u003c/h3\u003e\n\u003cp\u003eEthics approval for this study was reviewed by Human Research Ethics Committee at The University of Queensland (UQ) via the low-risk review pathway. The risk of breach of privacy was controlled via de-identification and minimized as only examiners (S.Z and S.A) had access to the identified records.\u003c/p\u003e\n\u003ch3\u003eSample selection\u003c/h3\u003e\n\u003cp\u003eThe total number of radiographs obtained from the university\u0026rsquo;s archives was 600. This sample size (n\u0026thinsp;=\u0026thinsp;193) was calculated using a single mean estimation with standard deviation (SD) of 14 units in maturity score and precision of 5 units.\u003c/p\u003e\n\u003ch3\u003eSelection process\u003c/h3\u003e\n\u003cp\u003eGood quality OPGs were selected (n\u0026thinsp;=\u0026thinsp;193) with all teeth in focus, of healthy individuals aged 5\u0026ndash;17 years from the archival records of the University of Queensland (UQ), School of Dentistry. Of the 193 radiographs, 49.7% were male and 50.3% were female. Excluded samples comprised of unclear radiographs, patients with hypodontia, hyperdontia, gross anomalies and pathologies (eg. Taurodontism, microdontia, Amelogenesis Imperfecta, Dentinogenesis Imperfecta, tumours, abscesses, cysts, fractures etc.), presence of gross caries, previous orthodontic treatment or severe malocclusion.\u003c/p\u003e\n\u003ch3\u003eDe-identification of radiographs\u003c/h3\u003e\n\u003cp\u003eThe dental record archives were reviewed by the two examiners (S.Z and S.A). Radiographs were selected based on the inclusion and exclusion criteria of the study as given in Fig.\u0026nbsp;1. Subjects who satisfied the criteria had their gender, date of birth and date of the radiograph collected. This data was de-identified and assigned a study identification number to maintain patient confidentiality. This study identification number is generated by a random number generator (RANDOM.ORG). The examiners maintained a separate link between the study identification and the patient\u0026rsquo;s electronic health record number. The de-identified radiographs were randomly assigned to each assessor.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAge estimation using the London Atlas\u003c/h2\u003e \u003cp\u003eThe de-identified radiographs were assessed by other investigators of the study (n\u0026thinsp;=\u0026thinsp;3) to determine the developmental and eruption stages of all teeth on the left side, in both upper and lower jaws, according to the London atlas (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Stage of development and eruption of each tooth was entered in the template table (Fig.\u0026nbsp;1). The estimated age was generated using the Lond Atlas Software (Queen Mary Innovation Ltd, London, United Kingdom) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe chronological age (real age) for each subject will be calculated by subtracting the date of the radiograph from the date of birth after having converted both to a decimal age using Eveleth and Tanner\u0026rsquo;s method (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The chronological age was blinded from the evaluators until after the age estimation was completed for all radiographs.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnalysis of data\u003c/h3\u003e\n\u003cp\u003eThe chronological age was subtracted from the estimated age, a positive result suggests an overestimation in age while a negative result suggests an underestimation. The mean difference of chronological age and estimated age of the whole sample was calculated. The mean difference for each age category was also calculated. A paired sample t-test was used to determine if the mean difference is significant. The mean difference in chronological age and estimated age for males and females were compared with the independent sample t-test. This will assess the accuracy of the London Atlas in age estimation between males and females. For all analyses, a p-value of less than 0.05 was considered a statically significant difference.\u003c/p\u003e\n\u003ch3\u003eInter-examiner and Intra-examiner reliability\u003c/h3\u003e\n\u003cp\u003eBefore the data collection process, assessors underwent an inter- and intra-examiner reliability test. A sample of 10 radiographs were assessed by each assessor. The same sample was also assessed by an examiner. Cohen\u0026rsquo;s kappa coefficient will be calculated to compare each of the student\u0026rsquo;s results against the examiner\u0026rsquo;s results for inter-examiner reliability. The radiographic sample was then reassessed by the assessors after an interval of 2 weeks to evaluate intra-examiner reliability, also using Cohen\u0026rsquo;s kappa.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eOf the 600 radiographs, only 193 satisfied the inclusion criteria, where 96 radiographs were of female subjects and 97 radiographs were of male subjects. Pre-data collection, inter-examiner and intra-examiner tests yielded Cohen\u0026rsquo;s Kappa scores of 0.89 and 0.81 respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInter and Intra-examiner kappa values\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRaters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInter-examiner Reliability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntra-examiner Reliability\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRater 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRater 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRater 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe mean estimated age for the collected sample was 11.56 years whilst the mean chronological age was 11.92 years. The mean difference of +\u0026thinsp;0.36 (standard deviation\u0026thinsp;=\u0026thinsp;1.67) was found to be significant (p\u0026thinsp;=\u0026thinsp;0.003). The overestimation of ages in age groups 6,7, 8, 10 and 11 were statistically significant, but were insignificant in age groups 9, 12, 13 and 15. The two largest overestimations (1.11 years and 1.05 years respectively) were found in age group 10 and 11.The atlas underestimated ages in groups 14 and 16, with the largest underestimation of 0.98 years in the 14-14.99 years age group, but the mean difference was statistically insignificant. Overall, the London Atlas was found to significantly over-estimate ages of the Australian children.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean difference between chronological and estimated age by age groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge group (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean difference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSD*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSig. (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSE*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e95% Confidence Interval of the Difference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003et-score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eLower\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eUpper\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll samples\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6-6.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7-7.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8-8.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9-9.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10-10.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11-11.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.393\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12-12.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13-13.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.409\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14-14.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15-15.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16-16.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.948\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e*SD \u0026ndash; Standard deviation; SE \u0026ndash; Standard error\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarises the mean age difference (difference between chronological and estimated age) in males and females where that of males was \u0026minus;\u0026thinsp;0.038, while that of females was 0.471, showing that the London Atlas overestimated ages of female subjects and underestimated ages of male subjects.\u003c/p\u003e \u003cp\u003eThe results for males and females were compared using an independent samples t-test (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The age difference between males and females is 0.509. However, the difference in variance and difference between the two genders were not significant (p\u0026thinsp;=\u0026thinsp;0.575 and p\u0026thinsp;=\u0026thinsp;0.110 respectively). Therefore, there was no significant difference in the accuracy of the London Atlas in estimating ages of males or females.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated and chronological age difference in males and females\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSex\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFemales\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMales\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStandard deviation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.772\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMinimum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-15.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaximum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003c/br\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Difference between estimated and chronological ages of male and female subjects\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"616\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndependent samples test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevene\u0026rsquo;s test for equality of variances\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" style=\"width: 369px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et-test for equality of means\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003esig*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edf*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean diff\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE diff*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDifference: males vs females\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEqual variances assumed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e191.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEqual variances not assumed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e168.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e*F \u0026ndash; Fisher ratio; sig \u0026mdash; significance; t\u0026mdash; t-value; df\u0026mdash;degrees of freedom; SE diff \u0026mdash; standard error difference; 95% CI \u0026mdash; 95% confidence interval\u003c/em\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eDental development has shown to be one of the more uniform processes in humans and is commonly associated with the actual age of an individual. Dental radiographs are often used to estimate dental age as it can be used in living individuals, is economical, easy to access and reliable (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This study investigated the reliability of the London Atlas in age estimation of the Australian population. This was carried out by measuring the difference in chronological and estimated age of the individual, which indicated either an overestimation or underestimation in age.\u003c/p\u003e \u003cp\u003eUsing the London Atlas, there was an underestimation in the age for males and overestimation in the age for females. This supports studies using Demirjian\u0026rsquo;s methods, where the overestimation and underestimation of ages were explained by slight discrepancies in the dental development between males and females (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). However, there was no significant difference in the accuracy of the London Atlas in estimating ages of males or females, and this coincides with results obtained from the study of the London Atlas in the Hispanic and Thai population (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe London Atlas was generally found to overestimate the ages of children in the Australian population. This was an average overestimation of 0.36 years, which equates to approximately four and a half months. This difference of four and a half months of age would not result in significant undesirable consequences, when applied to real world circumstances. The overestimation bias was statistically significant in several of the younger age groups, indicating that the atlas may be more inaccurate and overestimates more so in the younger population of Australian children. This slight overestimation was consistent with the results obtained when the London Atlas was applied to the Thai population (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Furthermore, in this study, children aged 10 and 11 had the largest over estimation (1.11 years and 1.05 years respectively). Recent studies on the Hispanic and Brazilian children population gave similar results. When the London Atlas was applied, Hispanic children aged 11 to 12 and Brazilian children aged 11 had one of the largest overestimations (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe average overestimation of 0.36 years from this study is consistent with the study conducted in Hispanic children using The London Atlas, where an average overestimation of 0.35 years was obtained (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). This result also concurs with the findings of the London Atlas in the Portuguese population, although the overestimated amount was one month, which was lesser than that in this study (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Additionally, The London Atlas during this study proved similar estimation to Demirjian\u0026rsquo;s methods previously tested in various Australian populations (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Flood et al. used Demirjian\u0026rsquo;s methods including more recent modified methods and found average age overestimation ranging from 0.31 to 0.61 years in a South Australian population (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). This method is usually found to be more time consuming due to more in depth analysis of each individual tooth.\u003c/p\u003e \u003cp\u003eThis study utilised an almost equal distribution of males (n\u0026thinsp;=\u0026thinsp;97) and females (n\u0026thinsp;=\u0026thinsp;96). Additionally, a significant number of radiographs were initially obtained to ensure a higher probability of members from the population to be included in the dataset. These radiographs were also filtered according to a strict inclusion and exclusion criteria, which produced 193 suitable radiographs.\u003c/p\u003e \u003cp\u003eThis study had several limitations. While the radiographs had strict inclusion and exclusion criteria, the quality of the radiographs could have varied. These images were taken on different machines in the facility and taken by different personnel. This non-standardised quality could have affected the interpretation of the developmental stages. The London atlas age estimation method could also be limited by the variation in the development of third molars. However, to this date, it is debatable whether the inclusion of third molars in age estimation affects the accuracy of the results (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). It is evident that third molars vary in development, morphology and positioning, and could potentially introduce more error in predicting one\u0026rsquo;s age (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Several studies even proposed that the rate of third molars developing, differs across different countries and ethnic groups (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). However, these studies concluded that the small differences in third molar development had little impact on age estimations due to the large standard deviation of developmental stages in each age group (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Additionally, this population-specific study targeting solely the Australian population, reduced the error of possible cross-population variability. The study of the London Atlas for age estimation in Portugese population also concluded that third molars should be used in age estimation as the results yielded are as reliable as using a whole set of teeth (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Another limitation of the London atlas is that it estimates the age to the precision of the midpoint of each year (e.g 6.5 years), whereas other studies including Demirijian\u0026rsquo;s methods, produces point estimates of age. However, the London Atlas has proven to be superior in accuracy when compared with most point estimate methods (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe difference between the dental age and chronological age might also not be attributed to the shortcomings of the respective dental age estimation methods. Similar to other biological processes of the human species, dental development also has a certain amount of variation (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Therefore, this difference might not be a limitation of the respective age estimation method but a limitation to using dental age as an estimation method (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is likely that technologies like machine learning and artificial intelligence (AI) will play a larger role in forensic age estimation moving forward, as other studies also suggest (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). However, we believe it is crucial to first grasp the foundational principles of this area of research before relying on advanced technologies, so as to better interpret the outcomes of automated methods. The London Atlas is emerging amongst many other age estimation methods. It is easily accessible in various languages and has comprehensive illustrations of various developmental stages and age categories. The results of the present study are comparable with other studies and also concludes The London Atlas as a reliable tool for age estimation. The London Atlas will need to be performed in different countries or ethnic groups to assess its robustness in age estimation across populations. One way of doing this would be to do a targeted study on Australian Aboriginals and Torres Strait Islanders versus Caucasoids. Reports have shown that Australian Aboriginals and Torres Strait Islanders have faster dental development than those of European descent (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). A comparison of the London Atlas and another age estimation method on the specific target group would be beneficial in determining which method is more accurate. Furthermore, obtaining a large enough sample was a difficulty this study faced. This could be made possible in the future by utilising radiographs from multiple Queensland Health facilities.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eAge estimation has significant relevance in current society. It guides decisions regarding child rights, justice, medico-legal disputes, migration and forensic science. The London Atlas is an emerging age estimation tool that is easily accessible, reproducible when used, and comprehensible. In this study, The London Atlas generally marginally overestimated the ages of Australian children and is equally accurate in estimating the age of males and females. Nevertheless, all current age estimation methods have shown its shortcomings. Overall, the London Atlas has comparable accuracy with other dental age estimation methods and should be considered as an adjunct tool for age estimation.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eDISCLOSURE STATEMENT \u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest to disclose. This research did not receive any financial support or funding from any external sources. All data generated or analysed during this study are included in the manuscript or are available from the corresponding author upon reasonable request. We confirm that all participants involved in this study provided informed consent to participate. In cases where participants were minors, consent was obtained from their legal guardians. Participants were fully informed about the study\u0026apos;s purpose, procedures, potential risks, and benefits before their inclusion. This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Ethical approval for this study was obtained from the University of Queensland Human Research Ethics Committee, and all procedures were performed in accordance with the ethical standards of the Declaration of Helsinki.\u003c/p\u003e\n\u003ch2\u003eAUTHOR CONTRIBUTIONS\u003c/h2\u003e \u003cp\u003eSZ and SA contributed to the study conception and design. Material preparation, data collection and analysis were performed by AC, SS, CH and EP. The first draft of the manuscript was written by AC and SA. The manuscript was later edited and reviewed by SS. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBerndt DC, Despotovic T, Mund MT, Filippi A (2008) The role of dentist in modern forensic age determination. Schw Monats Zahnm 118:1073\u0026ndash;1088\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLewis JM, Senn DR (2015) Forensic dental age estimation: an overview. J Calif Dent Assoc 43(6):315\u0026ndash;319. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/19424396.2015.12222856\u003c/span\u003e\u003cspan address=\"10.1080/19424396.2015.12222856\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManjunatha BS, Soni NK (2014) Estimation of age from development and eruption of teeth. J Forensic Dent Sci 6:73\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4103/0975-1475.132526\u003c/span\u003e\u003cspan address=\"10.4103/0975-1475.132526\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlQahtani SJ, Hector MP, Liversidge HM (2010) Brief communication: The London Atlas of human tooth development and eruption. Am J Phys Anthropol 142:481\u0026ndash;490. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ajpa.21258\u003c/span\u003e\u003cspan address=\"10.1002/ajpa.21258\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlQahtani SJ, Hector MP, Liversidge HM (2014) Accuracy of dental age estimation charts: Schour and Massler, Ubelaker, and the London Atlas. 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Ann Hum Biol 5(1):41\u0026ndash;54\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoorrees CF, Fanning EA, Hunt EE Jr (1963) Age variation of formation stages for ten permanent teeth. J Dent Res 42:490\u0026ndash;502\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Queensland","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"London Atlas, Dental age estimation, Australian population, Panoramic radiographs","lastPublishedDoi":"10.21203/rs.3.rs-6236330/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6236330/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAge estimation is a very essential tool that is required for quite a lot of purposes in legal settings and for disaster victim identification purposes. In the recent years, London Atlas for dental age estimation has gained popularity and its applicability in Australia has not been sufficiently validated. Therefore, a study was aimed to study the ability of London Atlas to accurately estimate the ages of an Australian cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAim\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo test the age prediction reliability of the London Atlas on an Australian population and to determine if there is a difference in its prediction accuracy between males and females.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 193 panoramic radiographs were accessed from the University of Queensland’s archival records. The London Atlas was used to estimate the dental ages of these radiographs of 96 females and 97 males, aged between 5 and 17 years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the entire cohort, the difference between the mean estimated age (11.56 years) and mean chronological (11.92 years) age was 0.36 years. This difference was found to be statistically significant (p \u0026lt; 0.001). The over-estimation of ages was significant in age groups 6, 7, 8,10 and 11. The mean age difference for males was 0.038 years while the difference for females was 0.471 years. However, the difference between the two sexes (0.509 years) was statistically insignificant (p \u0026gt; 0.001). The London Atlas shows a tendency to over-estimate ages of females and under-estimate ages of males.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe London Atlas was found to overestimate the ages of children in an Australian population by approximately four and a half months (0.37 years). However, there was no difference in age prediction accuracy between males and females. Overall, the London Atlas has comparable accuracy with other dental age estimation methods and should be considered as a tool for age estimation.\u003c/p\u003e","manuscriptTitle":"Reliability of London Atlas for Dental Age Estimation in an Australian Cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-24 12:07:16","doi":"10.21203/rs.3.rs-6236330/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4afbb521-a564-445a-b1a2-798ac5f1e6b0","owner":[],"postedDate":"March 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46121650,"name":"Forensic Medicine"},{"id":46121651,"name":"Dentistry"},{"id":46121652,"name":"Anthropology"}],"tags":[],"updatedAt":"2025-03-24T12:07:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-24 12:07:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6236330","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6236330","identity":"rs-6236330","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}