Open this publication in new window or tab >> Department of Stomatology, Public Health and Forensic Odontology, University of São Paulo (USP), Ribeirão Preto School of Dentistry, SP, Ribeirão Preto, Brazil.
Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Shanghai Forensic Service Platform, Ministry of Justice, Academy of Forensic Science, Shanghai, China.
Department of Dental Anthropology and Chair of Forensic Dentistry, University of Zagreb School of Dental Medicine, Zagreb and University Hospital Center Zagreb, Zagreb, Croatia.
Faculty of Dental Medicine, University of Lisbon, Lisbon, Portugal; Lisbon School of Medicine, University of Lisbon, Lisbon, Portugal; Centre of Statistics and Its Applications, University of Lisbon, Lisbon, Portugal.
Department of Radiology, Sunderby Research Unit, Luleå, Sweden.
Department of Pathology and Legal Medicine, University of São Paulo (USP), Ribeirão Preto Medical School, SP, Ribeirão Preto, Brazil.
Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Shanghai Forensic Service Platform, Ministry of Justice, Academy of Forensic Science, Shanghai, China.
Centre of Statistics and Its Applications, University of Lisbon, Lisbon, Portugal; School of Technology and Management, University of Leiria and West, Morro do Lena, Alto do Vieiro, Leiria, Portugal.
Department of Pathology and Legal Medicine, University of São Paulo (USP), Ribeirão Preto Medical School, SP, Ribeirão Preto, Brazil.
Jing’an District Dental Center, Shanghai, China.
Community Dentistry Department, School of Dentistry, University of São Paulo (FOUSP), São Paulo, São Paulo, Brazil.
Department of Oral Surgery, University of Split School of Medicine, Split, Croatia; Department of Oral Surgery, University Hospital of Split, Split, Croatia.
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2026 (English) In: International journal of legal medicine, ISSN 0937-9827, E-ISSN 1437-1596Article in journal (Refereed) Epub ahead of print
Abstract [en] Accurate forensic age estimation is critical in legal contexts where documentation is unavailable. The Third Molar Maturity Index (I3M) is a quantitative, reproducible method based on the ratio of the open apical width to the length of the mandibular third molar. This multicentre, cross-sectional study evaluated I3M performance for age-threshold classification at 18 years across five populations. A total of 3000 orthopantomograms from individuals aged 13–22 years were collected in Brazil, China, Croatia, Portugal and Sweden. Teeth 38 and 48 were measured, and binary classification (≥ 18 vs. <18 years) was assessed using ROC analysis, hierarchical logistic regression with likelihood ratio testing, and machine learning with leave-one-population-out (LOPO) validation. I3M showed excellent discriminatory performance in all five populations (AUC: 0.917–0.942 for tooth 38). Inter-population differences in I3M distributions were significant; population was an independent predictor of 18-year-old classification. Intra-population sex differences were significant in three populations, with females showing higher I3M values. Optimal cut-off values varied from 0.126 (Croatia) to 0.223 (China). LOPO validation confirmed robust cross-population generalisability (AUC: 0.917–0.958). I3M is a reliable age classification tool across diverse ethnic groups. Population-specific thresholds are recommended when ethnic background is known; when unknown, a conservative threshold of 0.10–0.15 is advised to minimise misclassification of minors.
Place, publisher, year, edition, pages
Springer, 2026
Keywords Age threshold, Forensic age estimation, I3M, Machine learning, Multi-ethnic, Third molar maturity index
National Category
Odontology
Identifiers urn:nbn:se:umu:diva-258067 (URN) 10.1007/s00414-026-03965-0 (DOI) 001853323900001 () 42618662 (PubMedID) 2-s2.0-105047781629 (Scopus ID)
2026-08-312026-08-312026-08-31