ArticleDiagnostics (Basel, Switzerland)2024
A Novel Algorithm for Forensic Identification Using Geometric Cranial Patterns in Digital Lateral Cephalometric Radiographs in Forensic Dentistry.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- An augmentation-enhanced transformer model for forensic identification using panoramic dental x-rays.International journal of legal medicine · 2026Article
- Sex estimation from lateral cephalograms via a hybrid multimodel convolutional neural network.Scientific reports · 2026Article
- Cranial Bone Repair and Regeneration After Trauma: Forensic and Clinical Medico-Legal Consequences.Bioengineering (Basel, Switzerland) · 2025Review
- Lateral Cephalograms as Valuable Antemortem Tools in Forensic Identification of Individuals during Mass Disasters: An Orthodontic Perspective.Journal of medical physicsArticle
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4 authors.
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Abstract
Lateral cephalometric radiographs are crucial in dentistry and orthodontics for diagnosis and treatment planning. However, their use in forensic identification, especially with burned bodies or in mass disasters, is challenging. AM (antemortem) and PM (postmortem) radiographs can be compared for identification. This study introduces and evaluates a novel algorithm for extracting cranial patterns from digital lateral cephalometric radiographs for identification purposes. Due to the unavailability of AM cephalograms from deceased individuals, the algorithm was tested using pre- and post-treatment cephalograms of living individuals from an orthodontic archive, considered as AM and PM data. The proposed algorithm encodes cranial patterns into a database for future identification. It matches PM cephalograms with AM records, accurately identifying individuals by comparing cranial features. The algorithm achieved an accuracy of 97.5%, a sensitivity of 97.7%, and a specificity of 95.2%, correctly identifying 350 out of 358 cases. The mean similarity score improved from 91.02% to 98.10% after applying the Automatic Error Reduction (AER) function. Intra-observer error analysis showed an average Euclidean distance of 3.07 pixels (SD = 0.73) for repeated landmark selections. The proposed algorithm shows promise for identity recognition based on cranial patterns and could be enhanced with artificial intelligence (AI) algorithms in future studies.
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