Three-dimensional superimposition of digital models for individual identification

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Highlights

  • The lowest RMS values of probable matches were all proven to be matches.

  • All of the RMS values of the matched models were below 0.27 mm.

  • Correntropy introduced into ICP could improve registration accuracy.

Abstract

Dentition is an individualizing structure in humans that may be potentially utilized in individual identification. However, research on the use of three-dimensional (3D) digital models for personal identification is rare. This study aimed to develop a method for individual identification based on a 3D image registration algorithm and assess its feasibility in practice. Twenty-eight college students were recruited; for each subject, a dental cast and an intraoral scan were taken at different time points, and digital models were acquired. The digital models of the dental casts and intraoral scans were assumed as antemortem and postmortem dentition, respectively. Additional 72 dental casts were extracted from a hospital database as a suspect pool together with 28 antemortem models. The dentition images of all of the models were extracted. Correntropy was introduced into the traditional iterative closest point algorithm to compare each postmortem 3D dentition with 3D dentitions in the suspect pool. Point-to-point root mean square (RMS) distances were calculated, and then 28 matches and 2772 mismatches were obtained. Statistical analysis was performed using the Mann-Whitney U test, which showed significant differences in RMS between matches (0.18 ± 0.03 mm) and mismatches (1.04 ± 0.67 mm) (P < 0.05). All of the RMS values of the matched models were below 0.27 mm. The percentage of accurate identification reached 100% in the present study. These results indicate that this method for individual identification based on 3D superimposition of digital models is effective in personal identification.

Introduction

Forensic odontology (FO) was described by Keiser‑Neilson as the “branch of forensic medicine which is in the interest of justice, deals with the proper handling and examination of dental evidence and also with proper evaluation and presentation of the dental findings” [1]. FO has been considered as one of the most credible and economical scientific methods for individual identification in mass disasters (MDs), as well as crimes and accidents [2]. In some MDs, the percentage of victims identified using the FO method ranges from 60.63%–100% [[2], [3], [4]].

Currently, DNA comparison technology is the most widely used and the most effective for individual identification, although it still has some constraints. However, the investigation of mixed traces remains a challenge to forensic DNA analysis [5]. It also should be noted that DNA testing is not perfect. Rare conditions such as identical twins, bone marrow transplants, and natural chimeras, as well as the absence of contrast samples due to discontinuities in kinship make it impossible to accurately identify individuals [6]. When it is difficult to use DNA information for individual identification, 3D dentition superimposition poses a good supplementary method.

Many theoretical studies and practical cases have proven that the image features of the frontal sinus, sphenoid sinus, sutures, and other skull structures can be used as individual identification indexes [[7], [8], [9]]. Some researchers have proposed different coding methods or classification systems for individual recognition based on the morphology of the frontal sinus or sphenoid sinus [[10], [11], [12], [13]]. Gibelli et al. [14] reported a correlation between morphological and metrical characteristics of frontal sinuses, and this may reduce the number of possible combinations and influence the reliability of these methods for personal identification.

Teeth are regarded as eligible postmortem (PM) material for individual identification. Dental enamel is the hardest tissue in the body and shows chemical stability. Teeth are protected by soft tissue and are highly resistant to high temperatures and PM destruction and decomposition [[15], [16], [17]].

When antemortem (AM) dental records are available, the process of comparative dental identification can be started. Traditionally, the methods for dental identification were based on manual comparison of dental records including medical history, dental casts, cone beam-computed tomography, and panoramic radiography. In addition, previous methods for anatomical or characteristic matching in dental identification were mainly based on encoding dentition, palatal rugae, or panoramic radiography [[18], [19], [20], [21]]. Since 2004, many studies [22,23] have developed different methods to encode teeth and dentition in panoramic photography, which can be used for individual recognition with over 90% accuracy. However, comparative dental identification relies on the experience of forensic odontologists, and the heavy workload might be a challenge to the identification process in MDs, resulting in low efficiency and errors by subjective observation. In addition, most dentition analysis methods are mainly based on two-dimensional (2D) images, which have low recognition rate compared with 3D images due to insufficient information.

Currently, segmentation of anatomical structures makes it possible to build 3D anatomical models from PM and AM, perform 3D superimposition, and achieve comparative dental identification based on quantitative analysis, which has been proven to be successful for frontal sinuses, sphenoid sinuses, and palatal rugae [[24], [25], [26], [27]]. Gibelli et al. in 2019 conducted 3D-3D superimposition of dental casts at the tooth level (upper first and second molars including both sides) with a small sample and proved its potential applicability to dental identification [28]. Franco et al. [29] and Chong et al. [30] used digital cast files to investigate the uniqueness of anterior teeth in forensic bitemark analysis. Reesu et al. [31] introduced automated 3D identification software into forensic identification and showed the effectiveness of automated software in forensic odontology. Reesu and Abduo conducted the registration of 3D models by an iterative closest point (ICP) algorithm in dental identification of digital models and dentition at the arch level and the tooth level, respectively [31,32].

However, the oral plaster model contains a large number of plaster tumors and bubbles, which could produce noise and outliers and influence the accuracy of the ICP algorithm. In the present study, 3D superimposition is based on integral upper dentition and conducted by an ICP algorithm. It is worth noting that, correntropy was introduced into the traditional ICP algorithm to address the noise and outliers in registration. The present study aimed to develop a 3D-3D superimposition approach for dental identification and to assess its discriminating potential using 3D models of dentition based on a novel registration algorithm.

Section snippets

Materials and methods

Twenty-eight college student volunteers (8 males and 20 females) aged between 21 and 27 years were recruited from Xi’an Jiaotong University. Another 72 patients (aged between 17 and 37 years) were selected from the database of the Department of Orthodontics, Stomatology Hospital of Xi’an Jiaotong University.

Informed consent was obtained from all volunteers. The exclusion criteria were as follows: ①systemic disease, syndromes and cleft lip and palate; ②maxillofacial deformities and pathologies;

Results

The lowest RMS values of two dentitions (probable matches) of the 28 retrievals were proven to be matches, and the precise rate of identification was 100%. The mean RMS of point-to-point distances between the superimposed dentitions was 0.18 ± 0.03 mm when these belonged to the same subjects, and 1.04 ± 0.67 mm when superimpositions were made with dentition from different individuals. The RMS value ranged between 0.13 and 0.27 mm in the case of matches, and between 0.38 and 8.79 mm in the case of

Discussion

Various methods based on oral tissues have been employed for individual identification, including craniofacial structure analysis, bite mark analysis, DNA analysis of teeth, and teeth and dentition analysis. Comparative dental identification serves as a vital part of individual identification in violent, disaster, or other mass tragedies and is economical, rapid, and effective [17]. However, PM dental profiles have received little attention in the field of forensics [[33], [34], [35]].

Palatal

Conclusions

The present study demonstrated the uniqueness of 3D dentition and showed an approach in individual identification based on an image registration algorithm in the upper arch. Successful identification could be achieved with high probability when the RMS value of the point-to-point distance of two dentitions is < 0.27 mm. In addition, there is a need to determine the distinguishing threshold of the upper and lower dentition in a larger dataset.

Compliance with ethical standards

All of the procedures in the present study involving human participants were performed in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. The current research was conducted after the approval of the Ethics Committee of Stomatology Hospital of Xi’an Jiaotong University, China. (Ethics Reference No: [2018] 016).

CRediT authorship contribution statement

Qing-nan Mou: Methodology, Data curation, Writing - original draft. Ling-ling Ji: Data curation, Writing - original draft. Yan Liu: Methodology, Writing - original draft. Pei-rong Zhou: Formal analysis, Data curation. Meng-qi Han: Formal analysis, Data curation. Jia-min Zhao: Formal analysis. Wen-ting Cui: Methodology. Teng Chen: Conceptualization. Shao-yi Du: Methodology, Writing - review & editing. Yu-xia Hou: Resources, Writing - review & editing, Supervision. Yu-cheng Guo:

Declaration of Competing Interest

The authors report no declarations of interest.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 81701869), and the China Postdoctoral Science Foundation (No. 2019M653664).

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