A new Computer Vision (CV) based method [1; 2] has been introduced for the automatic identification of unknown deceased individuals. This method involves extracting CV features from images, such as orthopantomograms (OPGs), and storing them in a CV database. CV features are distinctive visual attributes or characteristics extracted from images, enabling computers to analyze and understand visual information. The computer can recognize comparable CV features. If there is a high degree of similarity in CV features between the postmortem image and the antemortem reference image,...

All study methods received approval from the local ethics committee of Jena University Hospital (registration number 2019-1505-MV) and adhered to relevant guidelines. Funding was provided by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 514572362.The retrospective study analyzed 819 cranial computed tomography (CCT) examinations (age range 10-99 years, 321 female, 452 male and 46 unknown) of 722 individuals acquired between 11/2016 and 05/2023. Six regions were defined (refer to Figure 1), and their CT slices were manually selected from the CT series: lower row of...

A identification using CT images was achievable for approximately 72% to 87% (rank 1-10) of the identities. In all CT regions (A-F), same-individual identification achieved a score of 12.04 ± 0.86%, while different individuals scored 2.15 ± 0.40% (refer to Figure 2 and Table 1). This resulted in successful identification rates of 60 ± 8% (rank 1), 70 ± 8% (rank 5), and 74 ± 9% (rank 10). The maxillary sinus (region E) exhibited the highest success rates at 72% (rank 1), 80% (rank 5),...

Unambiguous identification of individuals based on a single CT slice is achievable. Metal artifacts, particularly from dental prosthetics, can hinder identification, with maxillary sinus CT slices showing the highest success rates.In future research, CT examinations of the thorax and abdomen will be explored, as these areas may offer additional distinctive CV features, thereby enhancing the success rate of CV-based human identification using CT images.

A. Heinrich: Nothing to disclose

Heinrich A, Güttler FV, Schenkl S, Wagner R, Teichgräber UKM (2020) Automatic human identification based on dental X-ray radiographs using computer vision. Scientific Reports 10:3801 Heinrich A, Güttler F, Wendt S et al (2018) Forensic Odontology: Automatic Identification of Persons Comparing Antemortem and Postmortem Panoramic Radiographs Using Computer Vision. Rofo 190:1152-1158 de Souza Jr LA, Marana AN, Weber SA (2018) Automatic frontal sinus recognition in computed tomography images for person identification. Forensic science international 286:252-264 Xavier TA, Terada ASSD, da Silva RHA (2015) Forensic application...