• prodi-gigi@fk.undip.ac.id
  • |
  • 024 – 76928010

SYSTEMATIC REVIEW CBCT in dental age estimation: A systematic review and meta analysis

1,2Rizky Merdietio Boedi, 3Simon Shepherd, 1Scheila Mânica and 1Ademir Franco

 

1Centre of Forensic and Legal Medicine and Dentistry, University of Dundee, Dundee, UK; 2Department of Dentistry, Universitas Diponegoro, Semarang, Indonesia; 3Oral Surgery, School of Dentistry, University of Dundee, Dundee, UK

 

Objectives: This study aimed to investigate the reproducibility of dental age estimation methods in cone beam computed tomography (CBCT) and the correlation between dental (DA) and chronological (CA) ages.

Methods: The scientific literature was searched in six databases (PubMed, Scopus, LILACS, Web of Science, SciELO, and OATD). Only observational studies were selected. Within each study, the outcomes of interest were (I) the quantified reproducibility of the method (k statis- tics and Intraclass correlation coefficient); and (II) the correlation (r) between the dental and chronological ages. A random-effect three-level meta-analysis was conducted alongside moderator analysis based on methods, arch (maxillary/mandibular), population, and number of roots.

Results: From 671 studies, 39 fulfilled the inclusion criteria, with one study reporting two different methods. The methods used in the studies were divided into metric (n = 17), volu- metric (n = 20), staging (n = 2), and atlas (n = 1). All studies reported high examiner repro- ducibility. Group 1 (metric and volumetric) provided a high inverse weighted r (δ = −0.71, CI [-0.79,–0.61]), and Group 2 (staging) provided a medium-weighted r (δ = 0.49, CI [0.44, 0.53]). Moderator analysis on Group one did not show statistically significant differences between methods, tooth position, arch, and number of roots. An exception was detected in the analysis based on population (Southeast Asia, δ = −0.89, CI [-0.94,–0.81]).

Conclusion: There is high evidence that CBCT methods are reproducible and reliable in dental

age estimation. Quantitative metric and volumetric analysis demonstrated better performance in predicting chronological age than staging. Future studies exploring population-specific variability for age estimation with metric and volumetric CBCT analysis may prove beneficial. Dentomaxillofacial Radiology (2022) 51, 20210335. doi: 10.1259/dmfr.20210335

 

Cite this article as: Merdietio Boedi R, Shepherd S, Mânica S, Franco A. CBCT in dental age estimation: A systematic review and meta analysis. Dentomaxillofac Radiol (2022) 10.1259/ dmfr.20210335.

Keywords: Cone Beam Computed Tomography; Age Determination by Teeth; Forensic dentistry; radiology; systematic review

 

Introduction

 

 

The development of diagnostic and therapeutic radiology in medicine have opened opportunities and potential advantages in Forensic Odontology. These improvements have enabled new perspectives in dental

 

Correspondence to: Rizky Merdietio Boedi, E-mail: rizkymerdietio@lecturer. undip.ac.id

Received 19 July 2021; revised 21 December 2021; accepted 21 December 2021;

published online 07 January 2022

age estimation, in particular improvements in data acquisition, image fidelity, and visualization of struc- tures.1 Greater access to the detail of complex anatom- ical structures, such as the human teeth, has allowed analytics such as linear regressions2 and  forensic statistic modelling.3 It is however  important  to  note that these advances in radiological techniques must be matched by skills development in viewing and inter- preting the resultant images. As contemporary forensic

 

odontology changes, forensic odontologists need to change.4

Forensic odontology has benefited mainly from the use of periapical5 and panoramic radiographs6 but with the advanced imaging, the cone beam computed tomog- raphy (CBCT), was introduced to the forensic practice. The advantage of CBCT over intraoral periapical and panoramic imaging in dental age estimation  comes from the availability of three-dimensional multiplanar navigation, allowing more detailed observation of morphological features. Among these features are pulp chamber size that demonstrates time-dependent reduc- tion in volume following the progressive deposition of secondary dentin. This phenomenon has great value in the estimation of age of  adults.7,8  Children and juve- nile populations are different and are usually studied by means of developmental parameters such as dental staging9 and measurements of tooth ratios.9

It must be emphasized that CBCT made significant

contributions Forensic Odontology.10–12 Studies have highlighted the application of CBCT for human identi- fication,10 bite-mark analysis,12,13 and dental age estima- tion14–17 among other disciplines. As the literature grows, new evidence is presented that raises ever more perti- nent questions. Consequently, systematic reviews and meta-analyses have become more common as a tool to extract data and build evidence-based answers. Between 2017 and 2021, several systematic reviews on dental age estimation were published.8,18–20 The studies had in common the stratified population targeted for  dental age estimation. In other words, Sehrawat et al.,19 Yusof et al,18 and Franco et al20 revisited age estimation studies in children and adolescents, while Marroquin et al inves- tigated adults.8 The outcomes of the previous systematic reviews were able to indicate best-fitting methods for a specific population,20 and the overall performance of a single method in populations worldwide.18 Hence, the present study is justified to bridge the gap of system- atic reviews of techniques from CBCT images, namely metric, volumetric, and staging analyses. Some of these analyses—especially volumetric—are described in the literature as time-consuming and suboptimal for appli- cation in practice,8 deserving a deep and dedicated look that could lead to evidence-based answers to whether they are reproducible and reliable enough for dental age estimation.

In order to further understand whether accurate

dental age estimation is possible using CBCT, the objective of this systematic review was to investigate the intra- and interobserver reproducibility and the r– value between visualized 3D dental parameters and the chronological age (CA). The set research question was: Are CBCT methods reproducible and reliable for dental age estimation?

Methods and materials

 

Eligibility criteria

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used.21 The research question stated in the previous  section was established based on  population  (P),  exposure (E), comparison (C), and outcome (O) as follows: p = human population; E = the CBCT methods used for dental age estimation (i.e., metric/volumetric, staging); C = the CA; and O = the quantified reproducibility and the r-value between the dental and CA (O).

 

Information sources

An electronic search (16 April 2021–19h April 2021) was performed in five databases to find out primary data: PubMed, Scopus, LILACS, Web of Science, and SciELO. Grey literature was collected from Open Access Theses and Dissertation (OATD) to reduce publication bias.

 

Search strategy

Search strings were built based on Medical Subject Head- ings (MeSH), Descriptors in Health Sciences (DeCS), and Emtree terms using associated key words (Table 1) combined with Boolean operators (i.e., “AND” and “OR”) and truncation (*) strategies. The keywords were associated with dental age estimation and CBCT using database-specific terms, synonyms, and their variations.

 

Selection process

Study eligibility was based on the following inclusion criteria: in vivo studies providing reproducibility and r– value between dental parameters and CA using CBCT, no restriction for population, year of publication or language were applied. The exclusion criteria included other means of CT imaging (i.e., Multidetector CT), studies including populations with known systemic diseases and dental anomalies, books, book chapters, editorials, letters to the editor, case reports, case series, abstract, and systematic reviews.

Study selection was performed by a main reviewer supervised by two others—all forensic odontologists. The acquired data were registered in EndNote 20 (Thomson Reuters, Toronto, Canada). Within the soft- ware, different folders were created to allocate studies based on their database of origin. The first study selec- tion was done by automatic duplicate detection using an EndNote built-in function and reviewed manually by the main examiner. The remaining studies were exported to Microsoft Excel 365 (Microsoft Ltd, Washington, USA) using tab-delimited output tools in EndNote 20 and then curated manually. Based on the title and abstract infor- mation, the second study selection phase was performed. Titles and abstracts not related to the topic of interest were promptly excluded. In case of doubts, the article was maintained in the sample and progressed to the next phase. Every article removed during the progressive

 

 

Table 1   The query for database search

DATABASE                             SEARCH STRATEGY                                                                                                                    n

 

PubMed                                   ((“Cone-Beam Computed Tomography”[Mesh] OR CBCT OR “Cone Beam Computed Tomography” OR “Cone Beam CT”) AND (Dentition[Mesh] OR tooth OR teeth

OR dental) AND (“Age Determination by Teeth”[Mesh] OR “age estima*” OR “age determination” OR “age assessment” OR “dental age”))

Scopus                                    ALL ((“Cone Beam Computed Tomography” OR “Cone-Beam Computed Tomography” OR “Cone Beam CT” OR CBCT) AND (“Age Determination” OR “Age Assessment” OR “Age estimation”) AND (dental OR dentition OR tooth OR teeth))

LILACS                                   (“cone beam computed tomography” OR “cone beam computerized tomography” OR “cone beam computer assisted tomography” OR CBCT OR “cone beam CT”) AND (“age measurement” OR “age estimation” OR “age determination” OR “dental age”) AND tooth OR teeth OR dentition

SciELO                                    (“cone-beam computed tomography” or “CONE BEAM COMPUTED TOMOGRAPHY” or “CONE BEAM CT” or CBCT) AND (“AGE estimation” or “AGE DETERMINATION” or “AGE DETERMINATION” or “AGE

ASSESSMENT”) AND (tooth or teeth or dentition)

Web of Science                        ALL=((“Cone Beam Computed Tomography” OR “Cone-Beam Computed Tomography” OR “Cone Beam CT” OR CBCT) AND (“Age Determination” OR “Age Assessment” OR “Age estimation”) AND (dental or dentition or tooth or teeth))

57

 

 

 

532

 

 

6

 

 

 

3

 

 

 

71

 

Open Access Theses and Dissertation

“dental age estimation” OR “age determination” OR “age assessment” OR “age                                2

estimation” AND “cone-beam computed tomography” OR “CONE BEAM COMPUTED TOMOGRAPHY” OR “CONE BEAM CT” OR CBCT

 

Boolean operator [Mesh] indicates that the keyword needs to be pulled from MeSH and all its derivatives.

Boolean operator asterisk, or wildcard truncation, (*) indicates that the search results may be displayed if the previous query requirement was met (i.e., “age estima*” requesting a search within age estimation or age estimative)

 

 

selection was noted separately. Subsequently, the third study selection was accomplished by evaluating the full texts to check for their eligibility. Articles that remained after full-text exclusions underwent data collection process.

 

Data collection

Data to be collected consisted of authors’ names, year of publication, studied population, sample size, age range, observed tooth/teeth, CBCT device, software for image analysis, method for age estimation, the (intra- and interobserver) reproducibility of the reported method and the r-value between the assigned independent vari- able and the CA. In case of unclear data reported in the eligible studies, e-mails were sent to the corresponding authors requesting clarification.

 

Study risk of bias assessment

The selected studies were assessed with risk of bias assessment by Joanna Briggs Institute (JBI) for cross-

22

by integrating studies’ reports in dedicated statistical analyses.24 In this meta-analysis, the primary effect size used was the r-value. Fisher r-to-z transformation was used to convert r into a normal metric value.25 Further- more, the authors relied on a random-effects model to assume that the effect size of interest is distributed due to the influence of study characteristics.26 Normally, methods for handling effect size dependency are  to treat the effect size independently (i.e., coming from a different study); to take the average measure of multiple effect sizes; or even may select only one effect size per study.27 These common approaches will lead to infor- mation loss and false independence, implying a ques- tionable homogeneity within the study. To avoid these problems, a three-level meta-analysis model was applied. This model is an optimal approach to deal with effect size dependency using three levels of the model: vari- ance of reported effect size, the variance of effect size within a study, and variance between studies.28 Due to this approach, two types of heterogeneity measures were

used, variance due to difference within studies (T2  , I2 )

 

b     b

sectional studies.      Each study was classified by two                                                                                                     w     w

 

observers (RMB and AF) using the critical appraisal checklist, with positive answer divided into 49%, 50–70%, and above 70% for high, moderate, and low risk of bias. Furthermore, the eligible studies underwent analysis with Begg’s rank correlation test to investigate if publication bias was present.23

 

Meta-analysis

A meta-analysis was designed to estimate the mean and variance of underlying effects between multiple studies with the same research question. This goal was achieved

and the difference between studies (T2,  I2).

Synthesis methods

RStudio with metaphor statistical package was used in this study.29,30 RStudio is open-source statistical soft- ware capable of advanced analysis in multiple fields of statistics. For a three-level meta-analysis, we used the rma.mv function via the metaphor package. Further adjustment using the Restricted Maximum Likelihood as an estimator, and Knapp and Hartung’s adjustment were undertaken to reduce the number of unjustified

 

significant result due to the Z distribution.31 Consid- ering that the r-value has two separate directions in this study (negative r-value for inverse relation with CA, and positive r-value for linear relation with CA), we sepa- rated the analysis between them. Group 1 consisted of studies with a negative r-value, and Group 2 consisted of studies with a positive r-value. Subgroup analyses (also called moderator analysis) with categorical variables were conducted to investigate the effects of a potential moderator variable. Categorical moderators used in this analysis are methods (volumetric, metric), arch (maxil- lary or mandibular tooth), population (European, West Asia, East Asia, etc.), and number of roots (single or multiradicular tooth).

 

Certainty assessment

Two reviewers independently performed the analysis in each of the meta-analysis results based on their certainty using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) tool, which rated the studies within high, moderate, low, and very low certainty.32

Results

 

The initial literature search resulted in 671 studies, with

121 duplicates. An initial exclusion based on article type resulted in 503 studies. A total of 399 studies were excluded having reviewed the titles due to the absence of any relevance to the current research question. After reviewing the abstracts, 53 studies remained for full-text reading. Exclusion resulted from the following: use of periapical radiographs (n = 7), not reporting dental age estimation (n = 10), use of other means of CT (n = 9), use of panoramic radiographs (n = 14), use of lateral cephalometric radiographs (n = 1), literature reviews (n

= 2), use of magnetic resonance imaging (n = 1), use of subjects with systemic/local conditions/diseases (n = 3), and use of ex-vivo samples (n = 4). The final sample included 39 studies for the qualitative analysis and 33 studies for quantitative analysis (Figure 1).

 

Study characteristics

The results of the qualitative analysis can be found in Tables 2 and 3. The first eligible study using CBCT for

 

 

 

Figure 1  PRISMA flowchart for the systematic review

 

 

Table 2   Summary of the included studies

 

Study ID Authors Population n Age Range Tooth Sample
1 Yang et al. 2006 34 Belgian 19 23–70 U1, U2, U3, L1, L2, L3, L4
2 Wu et al. 2016 50 Chinese 420 15–84 U1
3 Nemsi et al. 2017 37 Tunisian 120 22–67 U3, U5
4 Helmy et al. 2020 17 Egyptian 187 21–50 U7 L7
5 Rai et al. 2016 49 Indian 60 20–85 U3
6 Pinchi et al. 2015 2 Italian 148 10–80 21
7 Elgazzar et al. 2020 63 Egyptian 200 15–60 U3 L3
8 Yang et al. 2020 61 Chinese 230 8,18–19,92 21 23
9 Biuki et al. 2017 54 Iranian 122 13–70 U1 U2 U3 L1 L2 L3
10 Ceena Denny et al. 2021 47 Indian 100 – U6 U7 L6 L7
11 Salemi et al. 2020 46 Iranian 300 14–60 13
12 Molina et al. 2021 64 Spanish 107 14–70 U1 U2 U3 L1 L2 L3 L4 L5
13 Penaloza et al. 2016 36 Malaysian 101 15–75 U1 U2 U5 L2 L3 L4
14 Koh et al. 2017 66 Malaysian 284 Above 20 L4
15 Asif et al. 2019a 41 Malaysian 73 15–23 L8
16 Al-Omoush et al. 2020 45 Jordanian 135 18–63 U8
17 Archana et al. 2018 9 Indian 100 12 – Above 18 U8 L8
18 Doni et al. 2021 48 – 160 20–70 L5 L6
19 Gulsahi et al. 2018 56 Turkish 204 Above 15 U1 U2 U3 L3 L4 L5
20 Haghanifar et al. 2019 42 Iranian 377 20–69 U1 U3 L1 L3
21 Andrade et al. 2019 60 Brazilian 116 13–70 U1 U3
22 Farhadian et al. 2019 40 Iranian 300 14–60 U3
23 Kazmi et al. 2019 65 Pakistan 717 15–65 23 33
24 Asif et al. 2019b 59 Malaysian 300 16–65 U3 11
25 Kazmi et al. 2021 12 Pakistan 717 15–65 23 33
26 Star et al. 2011 51 Belgian 111 10–65 U1 U2 U3 U4 U5 L1 L2 L3 L4 L5
27 Alsoleihat et al. 2017 38 Jordanian 155 18–58 L8
28 De Angelis et al. 2015 52 Italian 91 15–85 13
29 Adisen et al. 2018 35 Turkish 131 17–75 U3
30 Zhan et al. 2020 62 Chinese 392 16–76 21 22 23
31 Asif et al. 2018 55 Malaysian 110 16–65 U1
32 Zhang et al. 2019 58 Chinese 414 20–65 L8
33 Oscandar et al. 2018 57 Indonesian 180 6–50 L6
34 Porto et al. 2015 53 Brazilian 72 22–70 U1
35 Asif et al. 2020 15 Malaysian 191 7–14 U3
36 Różyło-Kalinowska et al. 2020 44 Polish 121 5–13 31 32 33 34 35 36 37
37 Ugur Aydin et al. 2019 43 Turkish 120 14–75 U1
38 Lee et al. 2017 39 South Korean 224 20–77 13
39 Buchanan 2019 67 Hispanic 250 8.5–20.7 Left or Right Region

L, Lower; U, Upper.

Number notation in Tooth Sample follows the Federation Dentaire Internationale (FDI) numbering.

 

 

dental age estimation was conducted in 2006.33 Across 39 studies, 40 methods were reported, with one study using two different methods.34 Dental age estimation methods were metric (n = 17),14,34–49 volumetric (n = 20),2,11,16,33,34,50–64 staging (n = 2),65,66 and atlas.67

Twenty-two  studies  reported   reproducibility values with inter- and intraclass correlation coeffi- cient,2,11,14,16,36,37,39,40,42–46,54,55,57–61,63,64 one study reported technical error measurement,35 two studies reported Cronbach’s  a,34,67    and  one  study  reported  Cohens’

k.67  All studies reported high agreement between and

within the observers regardless of the reproducibility measurement used. The intraclass correlation coeffi- cient reported ranged from 0.592 to 0.981 for metric and 0.856 to 0.998 for volumetric analysis. Interclass correla- tion coefficient ranged from 0.798 to 0.93 for metric and

0.63 to 1 for volumetric analysis. Three studies reporting reproducibility values by means of t-test33,48,49  and one by r-value62 were detected.

In metric methods, the r-value ranged from −0.094 to −0.978, while in volumetric methods, it ranged from

−0.24 to −0.985. Staging methods had a variation from

 

 

 

Table 3  Continuation on Included Studies Characteristics

 

 

Study ID             Device                  Software                Method         Parameters    r n       Min r       Max r

 

ICC

Inter                  Intra

 

 

1 3D Accuitomo iDixel V Pulp Tooth Ratio 1 – −0.54 – –
2 Galileos – M Kvaal Method 7 0.69 0.86 – –
3 Galileos Galileos Viewer M Pulp Dentine Ratio 3 −0.84 −0.85 0.976 0.993
4 PlanMeca ITK-SNAP 3.8 V Pulp Chamber Volume 6 −0.69 −0.82 0.917 0.979
5 Kodak 9000 KODAK Dental Imaging Software 6.8 M Pulp Tooth Ratio 1 – 0.42 – –
6 Scanora 3D OnDemand 3D V Pulp Tooth Ratio 1 – −0.76 0.99 –
7 Cranex 3D ITK-SNAP 3.8 V Pulp Chamber Crown Ratio 4 −0.90 −0.96 – –
8 PlanMeca MIMICS 21.0 V Pulp Tooth Ratio 6 −0.67 −0.88 0.989 0.973
9 NewTom VG MIMICS 10.01 V Pulp Tooth Ratio 13 0.53 0.85 – –
10 Promax 3D – M Tooth Coronal Index 1 – −0.65 0,592–0,730 –
11 Cranex 3D OnDemand 3D M Pulp Tooth Ratio 24 −0.16 −0.61 – 0.94
12 Promax 3D Planmeca Romexis 2.3.1.R V Pulp Crown Ratio 3 −0.40 −0.60 – 0,63–0,83
13 Kodak 9000-3D; i-CAT OSIRIX M Kvaal Method 41 −0.21 −0.65 – –
14 i-CAT i-CAT Vision S Gustafson Method modified by Olze 4 0.44 0.62 – –
15 i-CAT MIMICS M Open Apices Surface Area 1 – −0.92 – 0.9
16 – OnDemand 3D M Pulp Tooth Ratio 1 – −0.52 – 0,91–0,93
17 – Planmeca Romexis S Demirjian 5 0.48 0.56 – –
18 Scanora 3D OnDemand 3D M Tooth Coronal Index 2 −0.09 −0.18 – –
19 Kodak CS9300 3D DOCTOR V Pulp Tooth Ratio 6 0.15 0.53 0,81–0,9 0,85–0,93
20 Cranex 3D OnDemand 3D M Pulp Tooth Ratio 16 −0.33 −0.76 – –
21 KODAK K9500 ITK-SNAP 3.4 V Pulp Volume 2 −0.87 −0.88 0,994–0,9998 0,994–1
22 Cranex 3D OnDemand 3D M Pulp Tooth Ratio 8 −0.16 −0.78 0.99 0.99
23 Promax 3D Planmeca Romexis V Pulp Volume 2 −0.51 −0.51 0,912–0,965 0,945–0,995
24 i-CAT MIMICS V Pulp Tooth Ratio 6 0.68 0.84 0.968 0.945
25 Promax 3D Planmeca Romexis V Pulp Tooth Ratio 2 −0.64 −0.66 0,8–0,9 0.9
26 Scanora 3D Simplant Pro V Pulp Tooth Ratio 12 −0.24 −0.88 – –
27 – – M Pulp Tooth Ratio 1 – −0.36 – 0,85–0,87
28 i-CAT OSIRIX V Pulp Tooth Ratio 3 −0.51 −0.70 – –
29 Promax 3D 3D DOCTOR V Pulp Tooth Ratio 1 – −0.49 – –
      M Kvaal Method 1 – −0.33 – –
30 Promax 3D MIMICS V Pulp Tooth Ratio 6 −0.78 0.81 0,932–0,975 0,946–0,987
31 i-CAT MIMICS V Pulp Tooth Ratio 2 −0.80 −0.88 0.982 0.914
32 Pax-Zenith 3D “Open Source” V Pulp Enamel Ratio 3 −0.63 −0.69 0.856 0,911–0,937
33 Vatech ITK-SNAP 3.6 V Pulp Chamber Volume 2 −0.98 −0.99 – –
34 i-CAT NG i-CAT

Workstation; DentalSlice

 V Pulp Tooth Ratio 2 −0.39 −0.55 – –
35 i-CAT MIMICS 21.0;

3-Matics

 M Open Apices Surface Area 4 −0.97 −0.98 0.902 0.931
36 NewTom 5G XL NNT M Cameriere Open Apices Ratio 0 – – 0,711–0,981 0,798–0,988
37 i-CAT inVivo 5 M Pulp Tooth Ratio 1 – −0.62 – 0.869
38 – OnDemand 3D M Pulp Tooth Ratio 3 −0.60 −0.73 – –
39 CB MercuRay Anatomage A London Atlas – – – – –

(Continued)

 

 

 

Table 3  (Continued)

 

 

Study ID             Device                  Software                Method          Parameters    r n       Min r       Max r

 

ICC

 

Inter                  Intra

 

 

A, Atlas; ICC, Intra-Class Correlation Coefficient for Inter-and Intra-observer; M, Metric; Max r, Maximum r value reported; Min r, Minimum

r value reported; S, Staging; V, Volumetric; r n, Number of correlation coefficient (r) reported. Study ID corresponds with Table 2.

 

 

 

0.44 to 0.575. Five studies were conducted using East- Asian populations,38,49,57,60,61 six studies used European populations,2,33,43,50,51,63 three studies used North African population,16,36,62 two studies used South American populations,52,59 five studies used South Asian popu- lations,11,46,48,64,65 seven studies used Southeast Asian populations,35,40,54,56,58,66 nine studies used West Asian populations,34,37,39,41,42,44,45,53,55 one  study  only  reported the population ethnicity,67 and one study did not report the population origin.47 When it comes to arch position, seven studies used mandibular teeth,37,40,43,47,56,65,66 twenty used   maxillary   teeth,11,14,34,36,38,39,42,44,45,48,49,51,52,54,57–61,64 and twelve used both arches..2,16,33,35,41,46,50,53,55,62,63,67 Single-rooted teeth were used in twenty eight studie s,2,11,14,33–36,38,39,41,42,45,48–55,58–64,66 while seven studies used multirooted teeth,16,37,40,44,46,56,57 and four studies  used both single and multirooted teeth.43,47,65,67

 

Risk of bias in eligible studies

Two studies had a moderate risk of bias,44,48 while the other studies revealed low risk of bias (Table 4). The most common bias that affected the studies was not addressing the validity and reliability of the measured outcome—which is related to the presence/absence of methods’ reproducibility. Begg’s rank correlation test indicated that publication bias was not present in our systematic review (p > 0.05).

 

Meta-Analysis

Table 5 contains the multilevel meta-analysis results. Across 33 studies, there were 179 observations. Group 1 consisted of 31 studies with 170 nested effect sizes, and Group Table 6 2 consisted of two studies with nine nested effect sizes. Group 1 includes metric and volu- metric studies provides a high inverse weighted r (δ =

−0.71, CI [-0.79,–0.61]) with high certainty. Group 2had the staging methods and revealed a medium-weighted r (δ = 0.49, CI [0.44, 0.53]) with moderate certainty due to small number of studies

b
b

Considerable heterogeneity was observed in between study analysis for Group 1 (T2 = 0.24,  I2  = 0.85), and a small heterogeneity was observed within the study

w
w

(T2 = 0.13, I2 = 0.03) with significant Q test. Group 2 does not appear to have any heterogeneity between and within the study concluded with an insignificant Q test.

Moderator analysis

Table 6 displays the overall analysis conducted for the moderator variables for Group 1, all with high certainty except for population analysis with low certainty of evidence due to indirectness from the included studies.68 Due to a small number of included studies (n = 2), moderator variable analysis was not conducted in Group

  1. There is no significant difference in r-values between methods, tooth position in arch and number of roots. In the population analysis, Southeast Asian study popula- tions significantly differ among other populations (δ =

−0.89, CI [-0.94,–0.81]).

 

Discussion

 

The results show a promising capability of CBCT in dental age estimation. This tool was especially useful for the detailed volumetric measurement of morpho- logical dental features of adults. However, the Euro- pean Commission Guidelines on CBCT for dental and maxillofacial radiology discourages the use of CBCT in daily dental practice without a proper justification and ideal image optimisation.69 Furthermore, guide- lines describing the best practice for the use of  CBCT in forensic dental identification and dental age estima- tion are not available. These guidelines would be funda- mental for forensic examination of the living and could be helpful to aid researchers and forensic odontologists to conceptualisz the methods and their application while still considering limitations and biosafety.

This systematic review and meta-analysis were designed to review the reliability and reproducibility of  age estimation methods using CBCT. In general, high agreement was found in inter- and intraobserver analyses in each eligible study. Besides ICC approach to quantify  reproducibility,  alternative  methods consist of  technical error measurement,70   Cronbach’s

a71  (for continuous variables) and Cohens’ k (categor-

ical variables)72—these tests were detected throughout the eligible papers.34,35,67 However, another important finding is that multiple studies used different methods, namely t-tests33,48,49 and r-values.62 These methodolog- ical decisions could reduce the reliability of the method. Streiner reports this as a type III error, which is “getting the right answer to a question that no one is asking”.73 On the same note, using the r-value will mask the systematic bias since a large difference between the observation will

 

 

Table 4 Risk of Bias Assessment by Joanna Briggs Institute for Cross-Sectional Study

 

Study ID Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Total
1 ✓ ✓ ✓ ✓ ✓ ✓ x ✓ 87,50%
2 ✓ ✓ ✓ ✓ ✓ ✓ x ✓ 87,50%
3 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 100,00%
4 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 100,00%
5 x ✓ ✓ ✓ x x x ✓ 50,00%
6 x ✓ ✓ ✓ ✓ ✓ ✓ ✓ 87,50%
7 ✓ ✓ ✓ ✓ ✓ ✓ x ✓ 87,50%
8 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 100,00%
9 ✓ ✓ ✓ ✓ ✓ ✓ x ✓ 87,50%
10 ✓ x ✓ ✓ ✓ ✓ ✓ ✓ 87,50%
11 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 100,00%
12 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 100,00%
13 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 100,00%
14 ✓ ✓ ✓ ✓ ✓ ✓ x ✓ 87,50%
15 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 100,00%
16 ✓ x ✓ ✓ x x ✓ ✓ 62,50%
17 ✓ ✓ ✓ ✓ ✓ ✓ x ✓ 87,50%
18 ✓ x ✓ ✓ ✓ ✓ x ✓ 75,00%
19 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 100,00%
20 ✓ ✓ ✓ ✓ ✓ ✓ x ✓ 87,50%
21 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 100,00%
22 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 100,00%
23 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 100,00%
24 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 100,00%
25 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 100,00%
26 ✓ ✓ ✓ ✓ ✓ ✓ x ✓ 87,50%
27 ✓ ✓ x ✓ ✓ ✓ ✓ ✓ 87,50%
28 ✓ ✓ ✓ ✓ ✓ ✓ x ✓ 87,50%
29 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 100,00%
30 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 100,00%
31 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 100,00%
32 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 100,00%
33 ✓ ✓ x ✓ ✓ ✓ x ✓ 75,00%
34 ✓ ✓ x ✓ ✓ ✓ x ✓ 75,00%
35 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 100,00%
36 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 100,00%
37 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 100,00%
38 ✓ ✓ ✓ ✓ ✓ ✓ x ✓ 87,50%
39 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 100,00%

Q1. Were the criteria for inclusion in the sample clearly defined? Q2. Were the study subjects and the setting described in detail? Q3. Was the exposure measured in a valid and reliable way?

Q4. Were objective, standard criteria used for measurement of the condition?

Q5. Were confounding factors identified?

Q6. Were strategies to deal with confounding factors stated? Q7. Were the outcomes measured in a valid and reliable way? Q8. Was appropriate statistical analysis used?

not be detected as long as there is a consistent error in the measurement.72

Our findings concerning the correlation between observed dental parameters and CA revealed high- and medium-weighted r-values for Group 1 (metric and volumetric) and Group 2 (staging). Studies that used the metric analysis  used  ratio-based  measurements to overcome the angular distortion and to promote a systematization of the selection of measurement units (i.e., centimetres and  millimetres)  in  each  analysis. On the contrary, in volumetric studies, the methods depend on the capability and performance of the software. The earliest study by Yang et al. (2006) was conducted in a semi-automatic software.33 In the later studies, researchers commonly use region-growing tools and greyscale-threshold based volumetric analysis, which provide an automated segmentation,16,58 three- dimensional masking to incorporate further analysis,40 and a less time-consuming process.50 Improvements in software used in volumetric studies should be encour- aged to allow more accurate detected of anatomic limits by distinguishing adjacent voxels on an image. Whilst the variety of software available offers multiple possi- bilities of choice, this variety also serves to increase the methodological heterogeneity across studies. Forensic- dedicated freeware are encouraged so researchers can contribute with inputs and plug-ins to fulfil the experts’ needs in practice.

Volumetric assessment in CBCT relies heavily on the voxel size.74 CBCT image acquired with small voxel size may produce a higher fidelity image,75 but also may increase radiation dose depending on protocol.76 This side-effect might contradict the need for high-resolution images to create an accurate volumetric rendering in CBCT. Pauwels et al. (2015) stated that to preserve image quality, a lower radiation dose can be effectively achieved by selecting a smaller field of view.77 Oenning et al. (2018) proposed a new approach following the concept of image acquisition with radiation dose “As Low as Diagnosti- cally Acceptable being Indication-oriented and Patient- specific” (ALADAIP).78 ALADAIP principle creates a new perspective in dental imaging and moves imaging science to create a standard based on the clinical needs and patient care.79 Hence, an alternative dental age esti- mation method in CBCT needs to be explored with lower radiation dose, especially in the late adolescence and early adulthood—a time interval in which the number of age estimation requests to investigate the age of majority (16 and 18 in most countries) increases. In this context, forensic experts must know that the younger the indi- viduals, the higher risk of radiation-induced biological effects.80 This is one of the reasons why panoramic radio- graphs remain the most common image of choice for age estimation of children and adolescents. When it comes to the deceased, radiation dose is not relevant,81 but the lack of standardized protocols for (CB)CT image acqusition for age estimation reflects the heterogeneous scenario in Forensic Odontology.

 

 

Table 5   Results of Three-Level Meta-Analysis in Correlation Coefficient between Dental Parameters and Chronological Age

 

n (ES) 95% CI Q(df) T2

w

 I2

w

 T2

b

 I2

b

 Certainty Ratinga
Group 1 31(170) −0.71 −0.79 −0.61 6173.5615(169) 0.04 0.13 0.24 0.85 ⊕⊕⊕⊕ High
Group 2 2 (9) 0.49 0.44 0.53 4,7412(8) 0 0 0 <0.01 ⊕⊕⊕ Moderate

95% CI, confidence interval; ES, number of effect sizes ; n, number of studies.

 

d = weighted average effect size;

 

Q(df) = Q test for homogeneity and degrees of freedom.

 

T2= estimated systematic variance in within (w) or between (b) studies.

 

I2= percentage of systematic variance of the overall observed variance in within (w) or between (b) studies.

 

aCertainty is rated following the GRADE Certainty Assessment

 

 

A deeper look into the results was feasible by means of a three-level meta-analysis. More specifically, the analysis of Group 1 showed a high inverse-weighted r of −0.71 (CI [-0.79,–0.61]). However, through the

moderator analysis, no difference was observed between metric and volumetric assessments and the other a priori-defined moderating variables. In the present meta-analysis, statistically significant differences were

 

 

  n(ES)   95% CI   T2

w

   R2  

w

 T2

b

   R2   Certainty Rating*
Methods                          
Volumetric 18(64) −0.75 −0.84 −0.62   0.04   0.00   0.24   0.03  
Metric

Arch

 13(106) −0.66 −0.79 −0.47                 ⊕⊕⊕⊕ High
Mandibular 4 (6) −0.81 −0.93 −0.53   0.04   0.00   0.25   0.03  
Maxillary 18(79) −0.71 −0.81 −0.57                 ⊕⊕⊕⊕ High
Both

Population

 9 (85) −0.68 −0.82 −0.44                  
East Asian 4 (18) −0.73 −0.87 −0.48   0.04   0.01   0.1499   0.49  
European 5 (20) −0.60 −0.79 −0.30                  
North African 3 (13) −0.86 −0.94 −0.68                  
South American 2 (4) −0.73 0.91 −0.34                 ⊕ Low
South Asian 3 (5) −0.61 −0.83 −0.22                  
Southeast Asianb 6 (56) −0.89 −0.94 −0.81                  
West Asian

Number of Roots

 7 (52) −0.49 −0.69 −0.23                  
Single-rooted 24(154) −0.70 −0.78 −0.59   0.04   0.00   0.23   0.01 ⊕⊕⊕⊕ High
Multi rooted 6 (14) −0.82 −0.91 −0.64                  

 

Table 6   Results of Three-Level Meta-Analysis in Correlation Coefficient between Dental Parameters and Chronological Age.

 

b

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

95% CI, confidence interval; ES, number of effect sizes; n, number of studies.

 

d = weighted average effect size;

 

Q(df) = Q test for homogeneity and degrees of freedom.

 

T2= estimated systematic variance in within (w) or between (b) studies.

 

R2= percentage of systematic variance of the overall observed variance in within (w) or between (b) studies.

 

aCertainty is rated following the GRADE Certainty Assessment

bsignificant value (p < 0.05)

 

detected involving population-specific comparisons of samples. Specifically, individuals from Southeast Asia differed significantly from other populations. This event might be explained by the amount of reported effect size (n = 56) in the study and the use of a novel technique proposed by the authors (-0.92 to −0.98).14,40 Oscandar et al. (2018) and Helmy et al. (2020) also acquired high inverse r-value, ranging from −0.98 to −0.9957 and −0.69 to −0.82,16 respectively. Although this moderating vari- able was significant, it needs to be interpreted carefully. It is important to note that employing  a  staging method for a specific modality creates a better model rather than metric or volumetric measurements in devel- oping dentition.82 Furthermore, the moderator analysis was limited on explaining the variability in Group 1 due to the small number of studies in Group 2. Considering the heterogeneity present in Group 1, future studies might seek to more thoroughly explore this issue since there is an underlying cause of high heterogeneity

b

(I2 = 0.85). Other recommendations for future studies include the need to set proper statistic methods to quan- tify the reproducibility in dental age estimation studies (to avoid neglecting systematic errors), and the need to standardize the protocols of observational studies following uniform guidelines, such as STROBE.

Conclusion

 

There is high evidence that CBCT methods are repro- ducible and reliable in dental age estimation. Volu- metric and metric methods presented a high certainty of evidence with the highest weighted r-value for dental age estimation with no significant difference. High certainty was also observed in the moderator analysis variables, except for population due to the indirectness of evidence. The assessment of volumetric morphological character- istics using CBCT provides a significant improvement in the accuracy of dental age estimation in adults. The eligible articles revealed lack of standardized methods, especially when it comes to the quantification of exam- iner reproducibility.

 

Acknowledgment

 

The author would like to thank Dr Muhammad Khan Asif, Rehman College of Dentistry and Dr Gavin Revie, University of Dundee for their respective study data and Dr Holger Steinmetz, Leibniz-Institute for Psychology for the statistical support.

 

Funding

 

This work was supported by  Ministry  of  Education and Culture, Universitas Diponegoro No.497/UN7.P/ HK/2021.

 

 

 

REFERENCES

 

 

  1. Willems G. A review of the most commonly used dental age esti- mation J Forensic Odontostomatol 2001; 19: 9–17.
  2. Pinchi V, Pradella F, Buti J, Baldinotti C, Focardi M, Norelli A new age estimation procedure  based  on  the  3d  cbct  study of the pulp cavity and hard tissues of the teeth for forensic purposes: a pilot study. J Forensic Leg Med 2015; 36: S1752- 928X(15)00182-1: 150–57: . https://doi.org/10.1016/j.jflm.2015. 09.015
  3. Thevissen PW, Fieuws S, Willems G. Human dental age estima- tion using third molar developmental stages: does a bayesian approach outperform regression models to discriminate between juveniles and adults? Int J Legal Med 2010; 124: 35–42. https:// org/10.1007/s00414-009-0329-8
  4. Engebretsen L, Steffen K, Bahr R, Broderick C, Dvorak  J, Janarv P-M, et The international olympic committee consensus statement on age determination in high-level young athletes. Br J Sports Med 2010; 44: 476–84. https://doi.org/10.1136/bjsm.2010. 073122
  5. Kvaal SI, Kolltveit KM, Thomsen IO, Solheim T. Age estimation of adults from dental radiographs. Forensic Sci Int 28, 1995; 74: 175–85. https://doi.org/10.1016/0379-0738(95)01760-g
  6. Knell B, Ruhstaller P, Prieels F, Schmeling Dental age diagnos- tics by means of radiographical evaluation of the growth stages of lower wisdom teeth. Int J Legal Med 2009; 123: 465–69. https:// doi.org/10.1007/s00414-009-0330-2
  7. Akay G, Gungor K, Gurcan The applicability of kvaal methods and pulp/tooth volume ratio for age estimation of the turkish adult population on cone beam computed tomography images.

Aust J Forensic Sci August 7, 2017; 51: 251–65. https://doi.org/10. 1080/00450618.2017.1356872

  1. Marroquin TY, Karkhanis S, Kvaal SI, Vasudavan S, Kruger E, Tennant M. Age estimation in adults by dental imaging assess- ment systematic Forensic Sci Int 2017; 275: S0379- 0738(17)30108-1: 203–11: . https://doi.org/10.1016/j.forsciint. 2017.03.007
  2. Augusto D, Palmela Pereira C, Rodrigues A, Cameriere R, Salvado F, Santos R. Dental age assessment by i2m and i3m: portuguese legal age thresholds of 12 and 14 year Acta Stom- atol Croat March 15, 2021; 55: 45–55. https://doi.org/10.15644/ asc55/1/6
  3. Pereira JGD, Santos JBS, Sousa SP de, Franco A, Silva RHA. Frontal sinuses as tools for human identification: a systematic review of imaging methods. Dentomaxillofac Radiol 1, 2021; 50(5): https://doi.org/10.1259/dmfr.20200599
  4. Kazmi S, Shepherd S, Revie G, Hector M, Mânica S. Exploring the relationship between age and the pulp and tooth size in a cbct analysis. Aust J Forensic Sci February 18, 2021; 00: 1–12. https://doi.org/10.1080/00450618.2021.1882567
  5. Giri S, Tripathi A, Patil R, Khanna V, Singh V. Analysis of bite marks in food stuffs by cbct 3d-reconstruction. J Oral Biol Cran- iofac Res 2019; 9: 24–27. https://doi.org/10.1016/j.jobcr.2018.08. 006
  6. Marques J, Musse J, Caetano C, Corte-Real F, Corte-Real AT. Analysis of bite marks in foodstuffs by computer tomography (cone beam ct)–3d reconstruction. J Forensic Odontostomatol 1, 2013; 31: 1–7.

 

 

  1. Asif MK, Ibrahim  N,  Al-Amery  SM,  Muhammad  AMA, Khan AA, Nambiar    A  novel  method  of  age  estimation in children using three-dimensional surface area analyses of maxillary canine apices. Leg Med (Tokyo) 24, 2020; 44: S1344- 6223(20)30024-9: 101690: . https://doi.org/10.1016/j.legalmed. 2020.101690
  2. Franco A, Vetter F, Coimbra E de F, Fernandes Â, Thevissen P. Comparing third molar root development staging in panoramic radiography, extracted teeth, and cone beam computed tomog- Int J Legal Med 2020; 134: 347–53. https://doi.org/10.1007/ s00414-019-02206-x
  3. Helmy MA, Osama M, Elhindawy MM, Mowafey B. Volume analysis of second molar pulp chamber using cone beam computed tomography for age estimation in egyptian adults. J Forensic Odontostomatol 30, 2020; 3: 25–34.
  4. Maret D, Peters OA, Dedouit F, Telmon N, Sixou M. Cone-beam computed tomography: a useful tool for dental age estimation? Med Hypotheses 2011; 76: 700–702. https://doi.org/10.1016/j. 2011.01.039
  5. Mohd Yusof MYP, Wan Mokhtar  I,  Rajasekharan  S, Overholser R, Martens Performance of willem’s dental age esti- mation method in children: a systematic review and meta-analysis. Forensic Sci Int 2017; 280: S0379-0738(17)30341-9: 245. . https:// doi.org/10.1016/j.forsciint.2017.08.032
  6. Sehrawat JS, Singh M. Willems method of dental age estimation in children: a systematic review and meta-analysis. J Forensic Leg Med 2017; 52: S1752-928X(17)30131-2: 122–29: . https://doi.org/ 1016/j.jflm.2017.08.017
  7. Franco A, de Oliveira MN, Campos Vidigal MT, Blumenberg C, Pinheiro AA, Paranhos LR. Assessment of dental age estimation methods applied to brazilian children: a systematic review and meta-analysis. Dentomaxillofac Radiol 1, 2021; 50(2): 20200128. https://doi.org/10.1259/dmfr.20200128
  8. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The prisma 2020 statement: an updated guide- line for reporting systematic reviews. BMJ March 29, 2021; 372: https://doi.org/10.1136/bmj.n71
  9. Moola S, Munn Z, Tufanaru C, Aromataris E, Sears K, Sfetcu R, et al. Chapter 7: systematic reviews of etiology and risk. Joanna Briggs Institute Reviewer’s Manual The Joanna Briggs Institute 2017; 5.
  10. Begg CB, Mazumdar Operating characteristics of a rank correlation test for publication bias. Biometrics 1994; 50: 1088– 1101. https://doi.org/10.2307/2533446
  11. Field AP, Gillett How to do  a  meta-analysis.  Br  J  Math Stat Psychol 2010; 63: 665–94. https://doi.org/10.1348/ 000711010X502733
  12. Fisher RA. On the’probable error’of a coefficient of correlation deduced from a small Metron 1921; 1: 1–32.
  13. Steinmetz H, Knappstein M, Ajzen I, Schmidt P, Kabst R. How effective are behavior change interventions based on the theory of planned behavior? Zeitschrift Für Psychologie July 2016; 224: 216–33. https://doi.org/10.1027/2151-2604/a000255
  14. Assink M, Wibbelink Fitting three-level meta-analytic models in r: a step-by-step tutorial. TQMP October 1, 2016; 12: 154–74. https://doi.org/10.20982/tqmp.12.3.p154
  15. Cheung MWL. MetaSEM: an r package for meta-analysis using structural equation Front Psychol 2014; 5: 1521. https:// doi.org/10.3389/fpsyg.2014.01521
  16. Team R, In: RStudio, editor. PBC. RStudio: Integrated Develop- ment for Boston, MA,; 2020.
  17. Viechtbauer W. Conducting meta-analyses in r with the metafor J Stat Softw 2010; 36: 1–48. https://doi.org/10.18637/jss. v036.i03
  18. Knapp G, Hartung J. Improved tests for a random effects meta- regression with a single covariate. Stat Med 15, 2003; 22: 2693– https://doi.org/10.1002/sim.1482
  19. Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, et GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ 26, 2008; 336: 924–26. https://doi.org/10.1136/bmj.39489.470347.AD
  1. Yang F, Jacobs R, Willems G. Dental age estimation through volume matching of teeth imaged by cone-beam Forensic Sci Int 15, 2006; 159 Suppl 1: S78-83. https://doi.org/10.1016/j. forsciint.2006.02.031
  2. Adisen MZ, Keles A, Yorubulut S, Nalcaci Age estimation by measuring maxillary canine pulp/tooth volume ratio on cone beam ct images with two different voxel sizes. Aust J Forensic Sci May 24, 2018; 52: 71–82. https://doi.org/10.1080/00450618.2018. 1474947
  3. Marroquin  Penaloza    TY,    Karkhanis    S,    Kvaal     SI, Nurul F, Kanagasingam S, Franklin D, et al. Application of the kvaal method for adult dental age estimation using cone beam computed tomography (cbct). J Forensic Leg Med 2016; 44: S1752-928X(16)30135-4: 178–82: . https://doi.org/10.1016/j.jflm. 10.013
  4. Nemsi H, Haj Salem N, Bouanene I, Ben Jomaa S, Belhadj M, Mosrati MA, et al. Age assessment in canine and premolar by cervical axial sections of cone-beam computed tomography. Leg Med (Tokyo) 2017; 28: S1344-6223(16)30233-4: 31–36: . https:// org/10.1016/j.legalmed.2017.07.004
  5. Alsoleihat F, Al-Shayyab MH, Kalbouneh H, Al-Zer H, Ryalat S, Alhadidi A, et al. Age prediction in the adult based on the pulp- to-tooth ratio in lower third molars: a cone-beam ct study. Int J Morphol June 2017; 35: 488–93. https://doi.org/10.4067/S0717- 95022017000200017
  6. Lee S-M, Oh S, Kim J, Kim Y-M, Choi Y-K, Kwak HH, et Age estimation using the maxillary canine pulp/tooth ratio in korean adults: a cbct buccolingual and horizontal section image analysis. Journal of Forensic Radiology and Imaging June 2017; 9: 1–5. https://doi.org/10.1016/j.jofri.2016.12.001
  7. Farhadian M, Salemi F, Saati S, Nafisi Dental age estima- tion using the pulp-to-tooth ratio in canines by neural networks. Imaging Sci Dent 2019; 49: 19–26. https://doi.org/10.5624/isd. 2019.49.1.19
  8. Asif MK, Ibrahim N, Al-Amery SM, John J, Nambiar P. Juve- nile versus adult: a new approach for age estimation from 3-dimensional analyses of the mandibular third molar Journal of Forensic Radiology and Imaging December 2019; 19: 100347. https://doi.org/10.1016/j.jofri.2019.100347
  9. Haghanifar S, Ghobadi F, Vahdani N, Bijani Age estima- tion by pulp/tooth area ratio in anterior teeth using cone-beam computed tomography: comparison of four teeth. J Appl Oral Sci 12, 2019; 27: e20180722S1678-77572019000100471. https://doi. org/10.1590/1678-7757-2018-0722
  10. Uğur Aydın Z, Bayrak S. Relationship between pulp tooth area ratio and chronological age using cone-beam computed tomog- raphy images. J Forensic Sci 2019; 64: 1096–99. https://doi.org/10. 1111/1556-4029.13986
  11. Różyło-Kalinowska I, Kalinowski P, Krasicka  E,  Galić  I, Mehdi F, Cameriere R. The cameriere method using cone-beam computed tomography (cbct) scans for dental age estimation in Aust J Forensic Sci July 13, 2020; 2020: 1–15. https://doi. org/10.1080/00450618.2020.1789221
  12. Al-Omoush SA, Alhadidi A, Al-Kayed A, Saoud H, Alsoleihat F. Do upper third molars provide more accurate age estimation in the adult based on the pulp-to-tooth ratio than lower third molars? a cone-beam ct Saudi Dent J 2021; 33: 702–6. https://doi.org/ 10.1016/j.sdentj.2020.04.006
  13. Salemi F, Farhadian M, Askari Sabzkouhi B, Saati S, Nafisi N. Age estimation by pulp to tooth area ratio in canine teeth using cone-beam computed tomography. Egypt J Forensic Sci January 15, 2020; 10: https://doi.org/10.1186/s41935-019-0176-9
  14. Ceena Denny E, Bastian T, Natarajan S, Thilak N, Binnal The coronal pulp cavity index an aid in age determination-a cone beam computed tomography study. Indian J Forensic Med Toxicol 2021; 15.
  15. Doni BR, Patil SR, Agrawal R, Ghazi N, Araki K, Dewangan G, et Tooth coronal index: a novel tool for age estimation on cone- beam computed tomography. Pesqui Bras Odontopediatria Clín Integr 2021; 21. https://doi.org/10.1590/pboci.2021.057

 

 

  1. Rai A, Acharya AB, Naikmasur VG. Age estimation by pulp- to-tooth area ratio using cone-beam computed tomography: a preliminary J Forensic Dent Sci 2016; 8: 150–54. https:// doi.org/10.4103/0975-1475.195118
  2. Wu Y, Niu Z, Yan S, Zhang J, Shi S, Wang T. Age estimation from root diameter and root canal diameter of maxillary central incisors in chinese han population using cone-beam computed Int J Clin Exp Med 2016; 9: 9467–72.
  3. Star H, Thevissen P, Jacobs R, Fieuws S, Solheim T, Willems G. Human dental age estimation by calculation of pulp-tooth volume ratios yielded on clinically acquired cone beam computed tomog- raphy images  of  monoradicular    J  Forensic  Sci  2011; 56 Suppl 1: S77-82. https://doi.org/10.1111/j.1556-4029.2010. 01633.x
  4. De Angelis D, Gaudio D, Guercini N, Cipriani F, Gibelli D, Caputi S, et al. Age estimation from canine volumes. Radiol Med 2015; 120: 731–36. https://doi.org/10.1007/s11547-015-0521-5
  5. Porto LVMG, Celestino da Silva Neto J, Anjos Pontual AD, Catunda RQ. Evaluation of volumetric changes of teeth in a brazilian population by using cone beam computed J Forensic Leg Med 2015; 36: S1752-928X(15)00143-2: 4–9: . https://doi.org/10.1016/j.jflm.2015.07.007
  6. Biuki N, Razi T, Faramarzi M. Relationship between pulp-tooth volume ratios and chronological age in different anterior teeth on J Clin Exp Dent 2017; 9: e688–93. https://doi.org/10.4317/ jced.53654
  7. Asif MK, Nambiar P, Mani SA, Ibrahim NB, Khan IM, Sukumaran P. Dentalageestimationemployingcbctscansenhanced with mimics software: comparison of two different approaches using pulp/tooth volumetric analysis. J Forensic Leg Med 2018; 54: S1752-928X(17)30203-2: 53–61: . https://doi.org/10.1016/j. 2017.12.010
  8. Gulsahi A, Kulah CK, Bakirarar B, Gulen O, Kamburoglu Age estimation based on pulp/tooth volume ratio measured on cone- beam ct images. Dentomaxillofac Radiol 2018; 47(1): 20170239. https://doi.org/10.1259/dmfr.20170239
  9. Oscandar F, Rojiun Z, Ibrahim N, Supian S, Tharmalingam D, Malinda Y. Preliminaryresearch: correlationbetweenpulpchamber volume of mandibular first molar and chronological age on deuteromalay subrace. Internati Journ of Medic Toxicol & Lega Medici 2018; 21: https://doi.org/10.5958/0974-4614.2018. 00023.2
  10. Zhang Z-Y, Yan C-X, Min Q-M, Li S-Q, Yang J-S, Guo Y-C, et al. Age estimation using pulp/enamel volume ratio of impacted mandibular third molars measured on cbct images in a northern chinese population. Int J Legal Med 2019; 133: 1925–33. https:// org/10.1007/s00414-019-02112-2
  11. Asif MK, Nambiar P, Mani SA, Ibrahim  NB,  Khan  IM, Lokman NB. Dental age estimation in malaysian adults based on volumetric analysis of pulp/tooth ratio using cbct data. Leg Med (Tokyo) 2019; 36: S1344-6223(18)30071-3: 50–58: . https://doi. org/10.1016/j.legalmed.2018.10.005
  12. Andrade VM, Fontenele RC, de Souza AC, Almeida CA de, Vieira AC, Groppo FC, et al. Age and sex estimation based on pulp cavity volume using cone beam computed tomography: devel- opment and validation of formulas in a brazilian sample. Dento- maxillofac Radiol 2019; 48(7): https://doi.org/10.1259/ dmfr.20190053
  13. Yang Z, Fan L, Kwon K, Pan J, Shen C, Tao J, et al. Age estima- tion for children and young adults by volumetric analysis of upper anterior teeth using cone-beam computed tomography data. Folia Morphol (Warsz) 2020; 79: 851–59. https://doi.org/10.5603/FM. 0004
  14. Zhan M, Chen X, Shi L, Lu T, Fan F, Zhang K, et Age estima- tion in western chinese adults by pulp–tooth volume ratios using cone-beam computed tomography. Aust J Forensic Sci February 23, 2020; 53: 681–92. https://doi.org/10.1080/00450618.2020. 1729415
  15. Elgazzar FM, Elboraey MO, El-Sarnagawy GN. The accuracy of age estimation from pulp chamber/crown volume ratio of canines obtained by cone beam computed tomography images: an egyp-

tian study. Egypt J Forensic Sci 2020; 10: 40. https://doi.org/10. 1186/s41935-020-00212-4

  1. Molina A, Bravo M, Fonseca GM, Márquez-Grant N, Martín-de-Las-Heras Dental age estimation based on pulp chamber/crown volume ratio measured on cbct images in a spanish population. Int J Legal Med 2021; 135: 359–64. https:// doi.org/10.1007/s00414-020-02377-y
  2. Kazmi S, Mânica S, Revie G, Shepherd S, Hector Age estima- tion using canine pulp volumes in adults: a cbct image analysis. Int J Legal Med 2019; 133: 1967–76. https://doi.org/10.1007/s00414- 019-02147-5
  3. Archana M, Nandita K, Jose NP, Srikant N, Shweta Y. Evalua- tion of the reliability of age estimation using cone beam computed tomography (cbct). Jour of Punj Acad of Foren Med Toxicol 2018; 18: https://doi.org/10.5958/0974-083X.2018.00037.7
  4. Koh KK,  Tan  JS,  Nambiar   P,   Ibrahim   N,   Mutalik   S, Khan Asif Age estimation from structural changes of  teeth and buccal alveolar bone level. J Forensic Leg Med 2017; 48: S1752-928X(17)30036-7: 15–21: . https://doi.org/10.1016/j.jflm. 2017.03.004
  5. Buchanan H. Dental Age Estimation of a Southern Nevada Hispanic Non-Adult Population Employing the London Atlas to Evaluate Archival Orthodontic Cone Beam Computed Tomog- raphy (CBCT) University of Nevada, Las Vegas; 2019.
  6. Guyatt GH, Oxman AD, Kunz R, Woodcock J, Brozek J, Helfand M, et al. GRADE guidelines: 8. rating the quality of evidence–indirectness. J Clin Epidemiol 2011; 64: 1303–10. https:// org/10.1016/j.jclinepi.2011.04.014
  7. europa.eu [homepage on the Internet]. Evidence based Guide- lines on Cone Beam CT for dental and maxillofacial radiology: Office for Official Publications of the European Communities. 2012. Available from: https://ec.europa.eu/energy/sites/ener/files/ documents/172.pdf
  8. Perini TA, de Oliveira GL, de Oliveira FP. Technical error of measurement in Rev Bras Med Sporte 2015; 11: 86–90.
  9. Ercan I, Yazici B, Sigirli D, Ediz B, Kan I. Examining cronbach alpha, theta, omega reliability coefficients according to sample J Mod App Stat Meth May 1, 2007; 6: 291–303. https://doi. org/10.22237/jmasm/1177993560
  10. Aldridge VK, Dovey TM, Wade Assessing test-retest reliability of psychological measures. Eur Psychol October 2017; 22: 207–18. https://doi.org/10.1027/1016-9040/a000298
  11. Streiner DL. A shortcut to rejection: how not to write the results section of a Can J Psychiatry 2007; 52: 385–89. https://doi. org/10.1177/070674370705200608
  12. Maret D, Telmon N, Peters OA, Lepage B, Treil J, Inglèse JM, et al. Effect of voxel size on the accuracy of 3d reconstructions with cone beam ct. Dentomaxillofac Radiol 2012; 41: 649–55. https://doi.org/10.1259/dmf/81804525
  13. Palomo JM, Rao PS, Hans Influence of cbct exposure condi- tions on radiation dose. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2008; 105: 773–82. https://doi.org/10.1016/j.tripleo. 2007.12.019
  14. Davies J, Johnson B, Drage Effective doses from cone beam ct investigation of the jaws. Dentomaxillofac Radiol 2012; 41: 30–36. https://doi.org/10.1259/dmfr/30177908
  15. Pauwels R, Araki K, Siewerdsen JH, Thongvigitmanee Technical aspects of dental cbct: state of the art. Dentomaxil- lofac Radiol 2015; 44(1): 20140224. https://doi.org/10.1259/dmfr. 20140224
  16. Oenning AC, Jacobs R, Pauwels R, Stratis A, Hedesiu M, Salmon B, et al. Cone-beam ct in paediatric dentistry: dimitra project position statement. Pediatr Radiol March 2018; 48: 308– https://doi.org/10.1007/s00247-017-4012-9
  17. Oenning AC, Jacobs R, Salmon B, DIMITRA Research Group (http://www.dimitra.be). ALADAIP, beyond alara and towards personalized optimization for paediatric cone-beam Int J Paediatr Dent 2021; 31: 676–78. https://doi.org/10.1111/ipd.12797
  18. Pauwels R, Cockmartin L, Ivanauskaité D, Urbonienė A, Gavala S, Donta C, et Estimating cancer risk from dental cone-

 

 

beam ct exposures based on skin dosimetry. Phys Med Biol 21, 2014; 59: 3877–91. https://doi.org/10.1088/0031-9155/59/14/3877

  1. Morgan Initiating a post-mortem computed tomography service: the radiologist’s perspective. Diagn Histopathol December 2010; 16: 556–59. https://doi.org/10.1016/j.mpdhp.2010.08.016

Thevissen PW, Fieuws S, Willems G. Third molar development: evaluation of nine tooth development registration techniques for age estimations. J Forensic Sci 2013; 58: 393–97. https://doi.org/ 10.1111/1556-4029.12063