The radiographic diversity of dental patterns for human identification: systematic review and meta-analysis

Preeji Girijan, Rizky Merdietio Boedi, Scheila Manica, Ademir Franco (Lead / Corresponding author)

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This study aimed to revisit the scientific literature related to the diversity of dental patterns observed in radiographs. The rationale was to find evidence to support dental human identifications. A systematic review was performed following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P). Strategic search was accomplished in five electronic data sources (SciELO, Medline/Pubmed, Scopus, Open Grey and OATD) were searched. The study model of choice was observational analytical cross-sectional. The search resulted 4.337 entries. The sequential screening based on title, abstract and full-text reading led to 9 eligible studies (n = 5.700 panoramic radiographs) published between 2004 and 2021. Studies from Asian countries were predominant (e.g., South Korea, China, and India). All the studies showed low risk of bias (measured according to the Johanna Briggs Institute’s critical appraisal tool for observational cross-sectional studies). Morphological, therapeutic, and pathological identifiers were charted from radiographs to create dental patterns across studies. Six studies (n = 2.553 individuals) had similar methodology and outcome metrics and were included in the quantitative analysis. A meta-analysis was performed and revealed a pooled diversity of the human dental pattern of 0.979 combining maxillary and mandibular teeth. The additional subgroup analysis with maxillary and mandibular teeth have a diversity rate of 0.897 and 0.924, respectively. The existing literature shows that human dental patterns are highly distinctive, especially if morphological, therapeutic and pathological dental features are combined. The diversity of dental identifiers found in the maxillary, mandibular and combined arches is hereby corroborated by this meta-analyzed systematic review. These outcomes support applications for evidence-based human identification.
Original languageEnglish
Article number102507
Number of pages9
JournalJournal of Forensic and Legal Medicine
Early online date27 Feb 2023
Publication statusPublished - Apr 2023


  • Dental patterns
  • Forensic dentistry
  • Human identification
  • Radiograph


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