Imaging Science in Dentistry

Korean Academy of Oral and Maxillofacial Radiology (대한영상치의학회)
권호 표지
DOI QR코드

DOI QR Code

Optimization of forensic identification through 3-dimensional imaging analysis of labial tooth surface using open-source software

  • Arofi Kurniawan (Department of Forensic Odontology, Faculty of Dental Medicine, Universitas Airlangga) ;
  • Aspalilah Alias (Department of Basic Sciences and Oral Biology, Faculty of Dentistry, Universiti Sains Islam Malaysia) ;
  • Mohd Yusmiaidil Putera Mohd Yusof (Center for Oral and Maxillofacial Diagnostics and Medicine Studies, Faculty of Dentistry, Universiti Teknologi MARA, Sungai Buloh Campus) ;
  • Anand Marya (Department of Forensic Odontology, Faculty of Dental Medicine, Universitas Airlangga)
  • Received : 2023.09.27
  • Accepted : 2024.01.13
  • Published : 2024.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

Purpose: The objective of this study was to determine the minimum number of teeth in the anterior dental arch that would yield accurate results for individual identification in forensic contexts. Materials and Methods: The study involved the analysis of 28 sets of 3-dimensional (3D) point cloud data, focused on the labial surface of the anterior teeth. These datasets were superimposed within each group in both genuine and imposter pairs. Group A incorporated data from the right to the left central incisor, group B from the right to the left lateral incisor, and group C from the right to the left canine. A comprehensive analysis was conducted, including the evaluation of root mean square error (RMSE) values and the distances resulting from the superimposition of dental arch segments. All analyses were conducted using CloudCompare version 2.12.4 (Telecom ParisTech and R&D, Kyiv, Ukraine). Results: The distances between genuine pairs in groups A, B, and C displayed an average range of 0.153 to 0.184mm. In contrast, distances for imposter pairs ranged from 0.338 to 0.522 mm. RMSE values for genuine pairs showed an average range of 0.166 to 0.177, whereas those for imposter pairs ranged from 0.424 to 0.638. A statistically significant difference was observed between the distances of genuine and imposter pairs(P<0.05). Conclusion: The exceptional performance observed for the labial surfaces of anterior teeth underscores their potential as a dependable criterion for accurate 3D dental identification. This was achieved by assessing a minimum of 4 teeth.

Keywords

References

  1. Putra RH, Astuti ER, Nurrachman AS, Putri DK, Ghazali AB, Pradini TA, et al. Convolutional neural networks for automated tooth numbering on panoramic radiographs: a scoping review. Imaging Sci Dent 2023; 53: 271-81.
  2. Fan F, Ke W, Wu W, Tian X, Lyu T, Liu Y, et al. Automatic human identification from panoramic dental radiographs using the convolutional neural network. Forensic Sci Int 2020; 314: 110416.
  3. Chen H, Sun C, Liao P, Lai Y, Fan F, Lin Y, et al. A fine-grained network for human identification using panoramic dental images. Pattern (N Y) 2022; 3: 100485.
  4. Fournier G, Savall F, Nasr K, Telmon N, Maret D. Three-dimensional analysis of bitemarks using an intraoral scanner. Forensic Sci Int 2019; 301: 1-5.
  5. Krishan K, Kanchan T, Garg AK. Dental evidence in forensic identification - an overview, methodology and present status. Open Dent J 2015; 9: 250-6.
  6. Verma AK, Kumar S, Rathore S, Pandey A. Role of dental expert in forensic odontology. Natl J Maxillofac Surg 2014; 5: 2-5.
  7. Pretty IA, Sweet D. A look at forensic dentistry - part 1: the role of teeth in the determination of human identity. Br Dent J 2001; 190: 359-66.
  8. 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 2013; 31: 1-7.
  9. Franco A, Willems G, Souza PHC, Bekkering GE, Thevissen P. The uniqueness of the human dentition as forensic evidence: a systematic review on the technological methodology. Int J Legal Med 2015; 129: 1277-83.
  10. Johnson A, Jani G, Carew R, Pandey A. Assessment of the accuracy of 3D printed teeth by various 3D printers in forensic odontology. Forensic Sci Int 2021; 328: 111044.
  11. Evans S, Jones C, Plassmann P. 3D imaging in forensic odontology. J Vis Commun Med 2010; 33: 63-8.
  12. Kitagawa T, Kohara H, Sohmura T, Takahashi J, Tachimura T, Wada T, et al. Dentoalveolar growth of patients with complete unilateral cleft lip and palate by early two-stage furlow and push-back method: preliminary results. Cleft Palate Craniofac J 2004; 41: 519-25.
  13. Kau CH, Richmond S. Three-dimensional analysis of facial morphology surface changes in untreated children from 12 to 14 years of age. Am J Orthod Dentofacial Orthop 2008; 134: 751-60.
  14. Kurniawan A, Hamdani J, Chusida A, Utomo H, Rizky BN, Prakoeswa BF, et al. Exploring the feasibility of smartphone cameras for 3D modelling of bite patterns in forensic dental identification. Leg Med 2024; 67: 102399.
  15. Reesu GV, Brown NL. Application of 3D imaging and selfies in forensic dental identification. J Forensic Leg Med 2022; 89: 102354.
  16. Varnavas A, Carrell T, Penney G. Fully automated 2D-3D registration and verification. Med Image Anal 2015; 26: 108-19.
  17. Avon SL. Forensic odontology: the roles and responsibilities of the dentist. J Can Dent Assoc 2004; 70: 453-8.
  18. Shah P, Velani PR, Lakade L, Dukle S. Teeth in forensics: a review. Indian J Dent Res 2019; 30: 291-9.
  19. Ata-Ali J, Ata-Ali F. Forensic dentistry in human identification: a review of the literature. J Clin Exp Dent 2014; 6: e162-7.
  20. Naether S, Buck U, Campana L, Breitbeck R, Thali M. The examination and identification of bite marks in foods using 3D scanning and 3D comparison methods. Int J Legal Med 2012; 126: 89-95.
  21. Casaglia A, DE Dominicis P, Arcuri L, Gargari M, Ottria L. Dental photography today. Part 1: basic concepts. Oral Implantol(Rome) 2016; 8: 122-9.
  22. Tugnait A, Clerehugh V. Gingival recession - its significance and management. J Dent 2001; 29: 381-94.
  23. Guttiganur N, Aspalli S, Sanikop MV, Desai A, Gaddale R, Devanoorkar A. Classification systems for gingival recession and suggestion of a new classification system. Indian J Dent Res 2018; 29: 233-7.
  24. GPL software. CloudCompare. http://www.cloudcompare.org/; 2023.
  25. Forrest A. Forensic odontology in DVI: current practice and recent advances. Forensic Sci Res 2019; 4: 316-30.
  26. Prajapati G, Sarode SC, Sarode GS, Shelke P, Awan KH, Patil S. Role of forensic odontology in the identification of victims of major mass disasters across the world: a systematic review. PLoS One 2018; 13: e0199791.
  27. Newcomb TL, Bruhn AM, Giles B, Garcia HM, Diawara N. Testing a novel 3D printed radiographic imaging device for use in forensic odontology. J Forensic Sci 2017; 62: 223-8.
  28. Prakoeswa BF, Kurniawan A, Chusida A, Marini MI, Rizky BN, Margaretha MS, et al. Children and adolescent dental age estimation by the Willems and Al Qahtani methods in Surabaya, Indonesia. Biomed Res Int 2022; 2022: 9692214.
  29. Reesu GV, Manica S, Revie GF, Brown NL, Mossey PA. Forensic dental identification using two-dimensional photographs of a smile and three-dimensional dental models: a 2D-3D superimposition method. Forensic Sci Int 2020; 313: 110361.
  30. Jani G, Johnson A, Parekh U, Thompson T, Pandey A. Effective approaches to three-dimensional digital reconstruction of fragmented human skeletal remains using laser surface scanning. Forensic Sci Int Synerg 2020; 2: 215-23.