A post-processing technique for cranial CT image identification

The current state of using post-mortem computed tomography for personal identification beyond odontology - A systematic literature review

Individual identification of unknown deceased is a vital function carried out by medical professionals, thus many tools have been developed or tested towards its end. One of the tools tested and still being tested is post-mortem computed tomography [PMCT]. This review aims to summarise the current state of using PMCT for personal identification beyond odontology. We found that most medicolegal researchers had a positive view of using PMCT for individual identification or for disaster victim identification. They have shown PMCT scans can be compared with a wide range of AM material - ante-mortem computed tomography [AMCT] scans, AM radiographs, or even textual AM medical history - for a successful identification. The use of textual medical history suggests the potential to create an artificial intelligence model that could quickly highlight areas of comparison. Anatomical body structures, pathological changes, or foreign bodies provide bases of comparison when using PMCT for individual identification. We found most (79 %) researchers have used qualitative methods to compare PMCT with AM material. Likewise, researchers so far have focussed on the axial skeleton (sans pelvis) when testing the viability of comparing specific body structures between AM material and PMCT scans. More body structures remain to be tested for their viability in personal identification, especially using quantitative methods.

A Novel Algorithm for Forensic Identification Using Geometric Cranial Patterns in Digital Lateral Cephalometric Radiographs in Forensic Dentistry

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.

Morphological analysis of three-dimensionally reconstructed frontal sinuses from Chinese Han population using computed tomography

The uniqueness and reliability of frontal sinuses for personal identification have gained wide recognition in forensics. However, few studies have assessed the usefulness of a three-dimensional (3D) model of the frontal sinus for human identification. This study aimed to develop standardized techniques to classify the frontal sinus according to its 3D morphological metrics and discover the usefulness of the 3D frontal sinus model in identification of Chinese Han population. One hundred and ninety-six computed tomography (CT) scans of patients older than 20 years (84 males and 112 females) were collected. A 3D frontal sinus digital model was segmented using Dolphin Imaging software. The following morphological metrics of the 3D frontal sinus were used to develop the coding system: bilateral or unilateral, spatial relationships of the two sides, number of septations, superior volume side, the shape of the 3D model of each side, shape of the medial surface and frontal ostium on each side, number of accessory septations on each side, number of supra-orbital cells of the medial surface and lateral surface on each side, and number of the arcades on each side. The new coding system accurately identified all of our research individuals. This study discovered a number of individual variations in the 3D frontal sinus morphology patterns. A coding system, which is based on these morphological patterns, exposes the morphological variants of frontal sinuses and presents the usefulness of 3D frontal sinus model for human identification.