Clinical Applicability of Various Digital Softwares Used in Forensic Odontology for Age Estimation Using Teeth on Cone-beam Computed Tomography Imaging: A Systematic Review and Meta-Analysis

Abstract

Background and Aim:

Forensic odontology plays a crucial role in age estimation and personal identification, especially in cases where only dental remains are available. In a similar context, various newer innovations in digital software are a key tool to assess age from such remains in contemporary times. The present systematic review and meta-analysis focuses on the use of various digital software used in forensic odontology for age estimation from dental remains in a range of studies that were published between 2006 and 2023.

Materials and Methods:

Relevant studies were sourced from Google Scholar, Directory of Open Access Journals, PubMed, U.S. National Institutes of Health/National Library of Medicine (NIH/NLM), Scimago Journal Ranking, SCOPUS, and Web of Science (WoS) using keywords such as forensic odontology, age estimation, and digital cone-beam computed tomography (CBCT), while only original research articles published between 2006 and 2023 were included. Again, digital software tools such as Adobe Photoshop CS2, AutoCAD, i-Dixel imaging software, ImageJ, ITK-SNAP 3.8, MIMICS, and OsiriX among others were evaluated for their precision and efficacy. The extracted data were organized in an Excel sheet including categories such as author, year, software used, sample size included, age estimation method used, age group included, teeth used, and location of the study, while the data obtained were subjected to statistical analysis.

Results:

The analysis revealed AutoCAD software to be the most common digital software used with a valid percentage of 31.5 and cumulative percentage of 48.2, followed by MIMICS and Adobe Photoshop CS2 to be the next in order. Furthermore, i-Dixel imaging software was the least common tool used in the list. Again, Kvaal’s and Demirjian’s methods were the most common methods employed, while pulp/tooth area ratio and pulp/tooth volume were the most common techniques used across the various studies published for age estimation in the defined period in descending order.

Conclusions:

Advancements in software tools and digital CBCT imaging have significantly improved the precision of age estimation in the field of forensic odontology. These tools possess enhanced features of image segmentation and three-dimensional reconstructions, leading to more accurate and reproducible results. Further research is, however, needed to standardize methods, refine population-specific equations, and improve the applicability of these techniques across diverse populations.

INTRODUCTION

Forensic odontology is a specialized field that plays a pivotal role in the examination, evaluation, management, and presentation of dental evidence in legal matters. This discipline is crucial for the purpose of identification, particularly in cases where only dental remains are available. The significance of this field is heightened in situations like mass disasters where conventional methods of identification might not be available or have failed due to any given reason. Furthermore, dental age estimation serves as an invaluable tool for law enforcement, assisting criminal investigations, missing persons’ and disaster victims’ identification, and for various other biomedical research applications.[1,2,3] Age estimation from teeth is based on the understanding that dental development follows specific patterns that vary with age. As individuals age, their teeth undergo specific morphological and physiological changes that can be analyzed to estimate their age.[4,5,6] In contemporary times, various methods for age estimation are available, which can be categorized into three broad categories, including the morphological, physiological (biochemical) and/or, the radiological methods. Among these, the morphological methods of age estimation involve analysis of the structural changes in teeth over a period of time including the age-related degenerative changes, also, known as the regressive alterations in teeth including attrition (wear), abrasion, erosions, and abfractions, while, also, the age-related degenerative changes seen in the periodontium including the inflammatory disease process recognized as periodontitis, secondary and tertiary (also, known as the reactive, reparative and/or, sclerotic) dentin deposition, root resorptions, root transparency and disordered and/or, abnormal cementum apposition.[2,3] Some of the prominent morphological methods of age estimation from teeth include the well-recognized Gustafson’s method,[7] Dalitz’s method,[8] Bang and Ramm’s method,[9] Johanson’s method,[10] and Maples’s[11] and Solheim’s[12,13,14,15,16,17] methods. On the contrary, biochemical methods of age estimation focus more on the chemical alterations in teeth as an individual gets older. In this pretext, one of the well-established biochemical methods of age estimation from teeth includes the assessment of amino acid racemization, which measures the conversion of amino acids from the L-form (the naturally occurring form) to the D-form, which is formed when an individual ages, with the ratio of D-to L-forms increasing in the elderly individuals, with this process of conversion occurring at predictable rates. The so-recognized D-and L-forms and/or configurations refer to the stereochemistry of chiral molecules that can rotate the polarized light in different directions, in clockwise (in case of D-forms) or anti- or, counter-clockwise (for L-forms) directions. The Helfman and Bada’s method[18,19,20] as well as Ritz-Timme et al.’s[21,22,23,24,25] method utilizes this technique to analyze dentin for age estimation. The radiological methods of age estimation from teeth, including the well-known Kvaal’s method,[26,27] Cameriere’s method[28,29,30,31,32,33,34,35,36,37,38,39] and Demirjian’s method,[40,41,42] rather provide a noninvasive and nondestructive method for age estimation. In this context, while the Kvaal’s and Cameriere’s methods measure pulp/tooth area ratio (PTR) on bitewing radiographs, the Demirjian’s method focuses on the developmental stages of third molar teeth to estimate the age. Furthermore, these methods are applicable to both living and deceased individuals and are widely used in various forensic investigations. With this background and because of the immensely innovative advancements in the imaging technologies and medical physics, a range of softwares has emerged in recent times to aid in the process of age estimation from teeth by carrying out analysis of the images acquired from different sources. These tools significantly enhance the precision and efficiency of forensic age estimation investigations.[43,44,45,46] In a similar context, recent software such as i-Dixel, ImageJ, ITK-SNAP 3.8, Materialise Interactive Medical Image Control System (MIMICS), OsiriX, Planmeca Romexis 6.1, and Simplant Pro-software facilitate image segmentation and three-dimensional (3D) reconstructions of dental structures using digital cone-beam computed tomography (CBCT) scans. These tools provide highly detailed and accurate images of dental structures, allowing for precise measurements of the PTRs as well as other age-related characteristics, facilitating accurate age estimation from the teeth and their remains.[47,48,49,50,51] These software programs have, indeed, advanced the field of forensic odontology by offering highly accurate, reliable, noninvasive, and nondestructive and also, reproducible methods for age estimation from the dental remains.[52,53,54,55,56] The aim of the present systematic review and meta-analysis was to critically evaluate the precision and efficacy of the various digital softwares used in forensic odontology for age estimation from dental remains in a range of studies that were published between 2006 and 2023 on the subject of forensic odontology with a particular focus on the application of digital CBCT and to assess how these tools have improved precision of measurements related to age-related characteristics used for dental age estimation for diverse forensic applications.

MATERIALS AND METHODS

Source of data

Relevant articles were sourced from Google Scholar, Hinari, Infotrieve, National Science Library, ProQuest, Directory of Open Access Journals (DOAJ), Emerging Sources Citation Index, PubMed, PubMed Central, the U. S. National Institutes of Health/National Library of Medicine (NIH), MEDLINE, Scimago Journal Ranking, SCOPUS, Web of Science (WoS), Science Citation Index Expanded, Elsevier, Wiley Online, and Cochrane Libraries using keywords related to forensic odontology and age estimation. Additional references were obtained through cross-referencing.

Data screening and segregation

Published studies were selected based on their relevance to forensic age estimation using dental remains, for both the living and deceased, while focusing on the recent innovations in the field of radiology and imaging, and medical physics. Again, only original research articles published in English literature between 2006 and 2023 were included, while secondary publications such as reviews, commentaries, and editorials were excluded from the study.

Search strategy

A combination of listed keywords including forensic age estimation, CBCT, digital imaging, medical physics, image segmentation, 3D reconstructions, digital software tools, Adobe Photoshop CS2, AutoCAD (computer-aided design [CAD]), i-Dixel imaging software, ImageJ, ITK-SNAP 3.8, MIMICS, OsiriX, Planmeca Romexis 6.1 software, Simplant Pro-software, Kvaal’s method,[26,27] Cameriere’s method,[28,29,30,31,32,33,34,35,36,37,38,39] Demirjian’s method,[40,41,42] Olze’s method,[57,58,59,60] PTR, pulp/tooth volume, tooth coronal index (TCI), and pulp chamber tooth height ratio (PCTHR) was used in search for relevant publications. Inclusion and exclusion criteria were applied accordingly.

Data extraction and management

The extracted data were organized in Excel sheets, including categories such as author, year, software used, sample size included, age estimation method used, age group included, teeth used, location of study, enhancement technique employed, image segmentation method employed, and performance/accuracy.

Statistical analysis used

Descriptive statistics were performed using Statistical Package for the Social Sciences (SPSS) version 23.0 (SPSS Inc., IBM Corporation, Armonk, NY, USA) statistical software, while frequency, percentage, valid percentage, and cumulative percentage were calculated. Again, Pearson’s correlation coefficient analysis, multiple regression analysis and analysis of Variance were performed to analyse the data statistically.

RESULTS

Table 1 and Graph 1 present the literature search strategy with keywords used for the same, while Table 2 and Graph 2 reveal the frequency of the various digital softwares used in studies published for age estimation in English literature between 2006 and 2023 wherein the analysis revealed a wide variety of software tools utilized for image segmentation and 3D reconstructions of dental structures using digital CBCT scans including Adobe Photoshop CS2, AutoCAD, i-Dixel imaging software, ImageJ, ITK-SNAP 3.8, MIMICS, OsiriX, Planmeca Romexis 6.1 software and Simplant Pro-software among others. Again, AutoCAD software was found to be the most common digital software used with a valid percentage of 31.5 and cumulative percentage of 48.2 followed by MIMICS and Adobe Photoshop CS2 softwares to be the next in order with the valid percentage for both being 16.7, and with the cumulative percentages being 87.0 and 16.7, respectively. In this list, i-Dixel imaging software was the least common software tool used with a valid percentage of 3.7 and a cumulative percentage of 51.9, being the newest digital software tool developed in the recent past. Table 3 shows the various demographics used in studies published for age estimation between 2006 and 2023 in the English literature which revealed a wide range of age groups spanning between 13 and 80 years, and sample size range studied, in the various studies from a minimum sample size of 20 to a maximum of 1772, selected from a wide array of geographic distribution. Regarding the age estimation methods used, Table 4 and Graph 3 show the commonly employed methods in studies published for age estimation between 2006 and 2023 that included the Kvaal’s, Demirjian’s, Olze’s and Cameriere’s methods used in descending order. Likewise, Table 5 and Graph 4 show the commonly employed techniques in the various studies published for age estimation between 2006 and 2023 which included PTR-all forms combined, pulp/tooth volume and isolated PTR apart from the TCI and PCTHR to be the most common techniques employed in descending order among others. Again, PTR was the most common technique employed in combination with one or more of the other methods to be closely followed next in order by pulp/tooth volume, while these techniques provided high precision even when analyzing a single tooth. Similarly, Table 6 and Graph 5 show the commonly employed teeth in different studies published for age estimation between 2006 and 2023 wherein maxillary and mandibular canines (all variations combined) followed by maxillary canines (all variations combined) and single-rooted teeth (all variations combined) were the most common teeth employed for age estimation studies, while this was, then, followed by mandibular premolars (all variations combined) in descending order with their valid and cumulative percentages as shown in the table 6.

Table 1.

Literature search strategy with keywords

Search strategy	Number of search results
Age Estimation, CBCT, Adobe Photoshop CS2 software	605
Age Estimation, CBCT, AutoCAD software	92
Age Estimation, CBCT, i-Dixel imaging software	291
Age Estimation, CBCT, ImageJ software	1140
Age Estimation, CBCT, ITK-SNAP 3.8 software	763
Age Estimation, CBCT, MIMICS software	635
Age Estimation, CBCT, OsiriX software	664
Age Estimation, CBCT, Planmeca Romexis 6.1 software	525
Age Estimation, CBCT, Simplant Pro-software	638

CBCT: Cone-beam computed tomography

Graph 1.

Literature search strategy with keywords

Table 2.

Commonly employed digital softwares in studies published for age estimation between 2006 and 2023

Descending order of the most common digital softwares used in studies published for age estimation between 2006 and 2023
Software used	Frequency	Percentage	Valid percentage	Cumulative percentage
AutoCAD software	17	31.48	31.48	48.15
MIMICS software	9	16.67	16.67	87.04
Adobe Photoshop CS2 software	9	16.67	16.67	16.67
ITK-SNAP 3.8 software	7	12.96	12.96	70.37
Simplant Pro-software	3	5.56	5.56	100.00
ImageJ software	3	5.56	5.56	57.41
Planmeca Romexis 6.1 software	2	3.70	3.70	94.44
OsiriX software	2	3.70	3.70	90.74
i-Dixel imaging software	2	3.70	3.70	51.85

Graph 2.

Commonly employed digital softwares in studies published for age estimation between 2006 and 2023

Table 3.

Various demographics used in studies published for age estimation between 2006 and 2023

Variable(s)	Associate(s)
Sample size range among various studies	20–1772
Population range among various studies	India, Australia, Belgium, Brazil, China, Egypt, France, Geneva, Indonesia, Iran, Korea, Lisbon, Malaysia, Nepal, Pakistan, Tunisia, Turkey
Age group range (years) among various studies	13–80 years

Table 4.

Commonly employed methods in studies published for age estimation between 2006 and 2023

Descending order of the most common methods used in studies published for age estimation between 2006 and 2023
Method used	Frequency	Percentage	Valid percentage	Cumulative percentage
Kvaal’s method	10	18.52	18.52	22.22
Demirjian’s method	2	3.70	3.70	31.48
Olze’s method	1	1.85	1.85	3.70
Cameriere’s method	1	1.85	1.85	1.85

Graph 3.

Commonly employed methods in studies published for age estimation between 2006 and 2023

Table 5.

Commonly employed techniques in studies published for age estimation between 2006 and 2023

Descending order of the most common techniques used in studies published for age estimation between 2006 and 2023
Technique used	Frequency	Percentage	Valid percentage	Cumulative percentage
PTR- all forms combined	35	64.81	64.81	100.00
Pulp/tooth volume	1	1.85	1.85	35.19
PTR	1	1.85	1.85	33.33

PTR: Pulp/tooth area ratio

Graph 4.

Commonly employed techniques in studies published for age estimation between 2006 and 2023

Table 6.

Commonly employed teeth in studies published for age estimation between 2006 and 2023

Descending order of the most common teeth used in studies published for age estimation between 2006 and 2023
Teeth and combination of teeth used	Frequency	Percentage	Valid percentage	Cumulative percentage
Maxillary and mandibular canines (all variations combined)	12	22.22	22.22	35.19
Maxillary canines (all variations combined)	9	16.67	16.67	87.04
Single-rooted teeth (all variations combined)	9	16.67	16.67	51.85
Mandibular premolars (all variations combined)	4	7.41	7.41	68.52
Maxillary and mandibular 3rd molars (all variations combined)	3	5.56	5.56	100.00
Maxillary and mandibular canines, premolars, and molars (all variations combined)	3	5.56	5.56	98.15
Left maxillary canine and left mandibular 2nd premolar	3	5.56	5.56	59.26
Maxillary and mandibular 1st/2nd molars (all variations combined)	3	5.56	5.56	11.11
Maxillary and mandibular incisors, canines, and premolars (all variations combined)	3	5.56	5.56	5.56
Right maxillary canine	1	1.85	1.85	92.59
Maxillary lateral incisors (all variations combined)	1	1.85	1.85	90.74
Maxillary incisors (all variations combined)	1	1.85	1.85	88.89
Maxillary anteriors (all variations combined)	1	1.85	1.85	70.37
Mandibular 2nd molars (all variations combined)	1	1.85	1.85	61.11
Left mandibular 3rd molars	1	1.85	1.85	53.70
Left maxillary and mandibular canines	1	1.85	1.85	12.96

Graph 5.

Commonly employed teeth in studies published for age estimation between 2006 and 2023

DISCUSSION

Forensic odontology plays an integral role in age estimation and personal identification, especially in situations where only the hard tissue remains, including dental remains are available.[1,2,3] The ability to estimate age from dental remains is vital in situations where other forms of identification, including fingerprints and/or, genetic information in the form of deoxyribonucleic acid are unavailable.[4,5,6] The present systematic review and meta-analysis provides an overview of the advancements made in the context of various digital software tools and imaging techniques used in forensic odontology, specifically, focusing on the period between 2006 and 2023 with an emphasis on the use of CBCT. The findings of the present systematic review underscore the significant role that softwares such as Adobe Photoshop CS2, AutoCAD, i-Dixel imaging software, ImageJ, ITK-SNAP 3.8, MIMICS, OsiriX, Planmeca Romexis 6.1, and Simplant Pro-software have played in enhancing the accuracy of dental age estimation by improving the precision of image segmentation and 3D reconstructions, an essential technique of layering of the images used in forensic analysis.

In a similar context, Adobe Photoshop CS2 is the standard digital software for digital image processing that scales and transforms images and vector illustrations in much easier and faster ways. It, indeed, has made possible implementation of the much demanded, nondestructive editing, and is the ninth official version of the Raster Graphics Editor developed and published by Adobe Inc., formerly, known as Adobe Systems Incorporated, an American computer software company based in San Jose, California for Web of Science (WOS).[61,62,63] Likewise, AutoCAD is a CAD software application developed by Autodesk that serves as an efficient digital image processing tool to create and document two-dimensional and 3D medical images.[64,65] Another significant tool in this row, the i-Dixel imaging software is a comprehensive 3D imaging digital software tool that offers a range of features from the initial exposure till final diagnosis, allowing for a thorough examination. The distinct features of i-Dixel imaging software include drawing functions along with volume rendering features, which convert 3D X-rays to solid images that can be rotated, magnified, and sliced in real time. This software tool, also, offers a unique ability to view both the CT images and volume rendered images at the same time.[66,67,68,69]

Again, the ImageJ software is an open‐source image analysis software platform that has aided researchers across the globe with a variety of image analysis applications, including the ability to visualize, inspect, quantify, and validate scientific image data. Historically, the predecessor to ImageJ, NIH Image, was developed by Wayne Rasband in the year 1987 at the National Institutes of Health and the Laboratory for Optical and Computational Instrumentation (LOCI, University of Wisconsin). ImageJ software facilitates image analysis techniques including image processing, colocalization (colocalization refers to the spatial overlap and potential correlation between the two or, more fluorescently labelled structures including different molecules, proteins and/or, cellular components in a biological sample, often, visualized using fluorescence microscopy), deconvolution (deconvolution is a process that is used in various fields including image processing, and that reverses convolution to recover or, estimate an original signal from its blurred or, distorted version to enhance image clarity and resolution), registration, image segmentation, tracking and/or, visualization.[70,71,72] On similar lines, ITK-SNAP 3.8 software is another free, open-source, multiplatform software application used for image segmentation in 3D and four-dimensional (4D) biomedical images using active contour methods. This software application was originally developed at the University of North Carolina by Guido Gerig (the NY University Tandon School of Engineering) with a limited feature centered specifically on image segmentation. The current ITK-SNAP software, though, has been developed at the Penn Image Computing and Science Laboratory (PICSL, University of Pennsylvania). ITK-SNAP software tool is, indeed, an open-source software application for medical image segmentation with its primary use in delineating anatomical structures and regions in CT, magnetic resonance imaging (MRI) and other 3D biomedical imaging data, while simultaneously providing tools for interpolating segmentations with improved Digital Imaging and Communications in Medicine (DICOM) support.[73,74,75]

Proceeding further, the Materialise Mimics software is another innovative image processing software for 3D designing and modelling that has been developed by Materialise NV, a Belgian company, specialized in additive manufacturing software and technology for medical, dental, and manufacturing industries. Mimics are an acronym for MIMICS. This software is used to create 3D surface models from stacks of 2D data obtained from sources such as CT, MicroCT, MRI, confocal microscopy, and X-ray and ultrasound using rapid prototyping technology along with computer-aided engineering (CAE), CAD, and surgical simulation technology through image segmentation procedures.[76,77] In this series, OsiriX software is another one of the most widely used DICOM viewer in the world, which offers advanced post-processing techniques in 2D and 3D, working in close conjunction with the Picture Archiving and Communication System software. OsiriX MD offers all modern post-processing techniques, such as multiplanar reconstruction and 3D rendering that includes maximum intensity projection to volume and surface rendering features. All these modes support 4D data and are able to produce image co-registrations or image fusion between two different series, such as positron emission tomography-CT (PET-CT) and single-photon emission computed tomography-CT (SPECT-CT). The OsiriX project, indeed, started in 2004 at the University of California with Dr. Antoine Rosset and Prof. Osman Ratib. The software per se, however, was developed by Rosset working in LaTour Hospital (Geneva, Switzerland) and Joris Heuberger, a computer scientist from Geneva. OsiriX has been specifically designed for navigation and visualization of multimodality and multidimensional images.[78,79,80]

Similarly, the Planmeca Romexis 6.1 3D software not only allows for capturing, viewing and processing of all data types including 2D and 3D images and CAD/computer-aided manufacturing cases, surface models and 3D photos in one interface in the same database, while, also, real-time monitoring possibilities along with a streamlined and quick image viewing, and provision of extensive specialist tools needed for an advanced treatment planning and analyses. Again, Simplant Pro-software provides axial, cross-sectional, and panoramic views of patient data and allows for reformatted CBCT images required for advanced treatment planning, and analyses without scatter and/or, any other artifacts.[81,82,83] The use of CBCT in the field of forensic odontology has, actually, revolutionized the entire imaging and analysis concept of age estimation by providing high-resolution 3D images of dental structures which allow for a detailed analysis of the PTRs and other critical morphological features that are highly useful in forensic age estimations. Digital CBCT scans have allowed for more accurate measurements compared to the conventional and traditional radiographic techniques, while the application of the various principles of medical physics have not only made it possible to obtain clearer, more detailed and high-resolution images of dental pulp; however, the 3D images of dental and pulp structures which is crucial in the process of age estimation.[43,44,45,46,47,48,49,52]

Furthermore, the PTR, a key method for age estimation, has emerged as the single most reliable technique in forensic research, while it involves measuring the ratio of pulp chamber area to the area of the entire tooth. The significant advantages of this method include it being a noninvasive and nondestructive method for dental age estimation, while this method can yield accurate results even when only a single tooth is analyzed.[1,28,29,31,32,35,36,37,38] Likewise, the analysis in the present systematic review revealed Kvaal’s,[26,27] Demirjian’s,[40,41,42] Olze’s[57,58,59,60] and Cameriere’s[28,29,30,31,32,33,34,35,36,37,38,39] methods to be the most common methods employed for age estimation in the various studies published between 2006 and 2023 in descending order. In this pretext, the continuous improvement seen in relation to the imaging technologies and softwares used has been crucial in enhancing the precision of the age estimation methods. As image quality continues to improve, so, too, the accuracy of age estimation methods. In a similar context, advancements in the software tools, particularly those used for image segmentation and 3D reconstructions have greatly enhanced the precision of measurements related to the pulp/tooth ratios and other key features used in the methodology employed for age estimation.

Softwares such as i-Dixel imaging software, ImageJ, ITK-SNAP 3.8 software, MIMICS, and OsiriX have already demonstrated their potential to significantly improve the analytical methods by offering endless possibilities for image processing, which in turn leads to more accurate and reliable results. While significant strides have been made in the field of forensic odontology, there is still much to be done. Future research must focus on refining the current methodologies used as well as standardizing the age estimation techniques used, while acquiring more population-specific data and equations to improve the precision and practical applicability of age estimation methodologies to be used across diverse populations. By leveraging advancements in the arena of imaging technologies and computational tools, and with appropriate placement and utility of the underlying principles of medical physics, the field of forensic odontology can continue to contribute in a much more effective manner for diverse clinical applications.

CONCLUSIONS

The present systematic review and meta-analysis underscore the significance of image segmentation and 3D reconstruction softwares such as i-Dixel imaging software, ImageJ, ITK-SNAP 3.8, MIMICS, and OsiriX among others, in advancing the discipline of forensic odontology for dental age estimation. Furthermore, the analysis of the studies included in the present review and meta-analysis revealed Kvaal’s and Demirjian’s methods to be the most common methods employed, while PTR and pulp/tooth volume as the most common techniques used across the various studies published for age estimation between 2006 and 2023 on the subject of forensic odontology in descending order offering accurate results with high precision.

Future research directions

The present systematic review and meta-analysis offers an insight into the possibility of integration of the various advanced image processing techniques and principles of medical physics with the conventional and established techniques and methods to significantly improve the accuracy of forensic age estimation from dental remains, especially, in situations like mass disasters wherein conventional methods have failed due to any given reasons. However, further studies are mandated to standardize the methodologies, increase the sample diversity, and refine more population-specific data and equations to further enhance the precision of age estimation methodologies. By optimizing these methods, the field of forensic odontology can contribute significantly to the vast domain of applications for carrying out legal proceedings and criminal investigations, in personal identification, and for various other broader biomedical applications.

Authors’ contributions

All the authors have participated in the conception of this systematic review and meta-analysis as well as in the analysis and interpretation of data, data elaboration, and critical reviews of the report, and have read and approved the final version of the manuscript. The authors confirm that there are no concerns of financial involvement with any organization(s), entities and/or, individuals with an interest in the subject matter or materials discussed in the manuscript, and that there are no conflicts of interest.

Conflicts of interest

There are no conflicts of interest.

Funding Statement

Nil.