3D Bitemark Analysis in Forensic Odontology Utilizing a Smartphone Camera and Open-Source Monoscopic Photogrammetry Surface Scanning

Arofi Kurniawan, An’Nisaa Chusida, Haryono Utomo, Maria Istiqomah Marini, Beta Novia Rizky, Beshlina Fitri Widayati Prakoeswa, Janice Hamdani, Rodrigo Salazar-Gamarra, Luciano Lauria Dib, Aspalilah Alias, Mohd Yusmiaidil Putera Mohd Yusof, Anand Marya

Research output: Contribution to journalReview articlepeer-review

4 Citations (Scopus)

Abstract

Bitemark analysis is a challenging procedure in the field of criminal case investigation. The unique characteristics of dentition are used to find the best match between the existing patterned injury and the suspected perpetrator in bitemark identification. Bitemark analysis accuracy can be influenced by various factors, including biting pressure, tooth morphology, skin elasticity, dental cast duplication, timing, and image quality. This review article discusses the potential of a smartphone camera as an alternative method for 3D bitemark analysis. Bitemark evidence on human skin and food should be immediately recorded or duplicated to retrieve long-lasting proof, allowing for a sufficient examination period. Various studies utilizing two-dimensional (2D) and three-dimensional (3D) technologies have been developed to obtain an adequate bitemark analysis. 3D imaging technology provides accurate and precise analysis. However, the currently available method using an intraoral scanner (IOS) requires high-cost specialized equipment and a well-trained operator. The numerous advantages of monoscopic photogrammetry may lead to a novel method of 3D bitemark analysis in forensic odontology. Smartphone cameras and monoscopic photogrammetry methodology could lead to a novel method of 3D bitemark analysis with an efficient cost and readily available equipment.

Original languageEnglish
Article numbere220087
JournalPesquisa Brasileira em Odontopediatria e Clinica Integrada
Volume23
DOIs
Publication statusPublished - 23 Feb 2023

Keywords

  • Dentition
  • Identity Recognition
  • Photogrammetry
  • Smartphone

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