ArticleThe Journal of forensic odonto-stomatology2021
Forensic dental identification in complicated fractured skull conditions: case report with adapted algorithm for image comparison.
Article in The Journal of forensic odonto-stomatology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveTo analyze the perspective of using an adapted algorithm for digital images comparison while providing forensic dental identification in complicated fractured skull conditions by ante-mortem and post-mortem radiographical data sets. MATERIALS AND
methodsAnte-mortem orthopantomogram and post-mortem peri-apical X-ray images were converted in *.jpeg format with their further import into GIMP 2.10 software (The GIMP Development Team). Segmentation of OPG-image was provided in topographical projections of jaw segments obtained directly from the victim. Comparison of analyzed image segments was provided manually within GIMP 2.10 software using functions of "Layers" and "Opacity" through the proposed algorithm.
resultsConsidering the fact that 20 positive concordant dental identifiers overall were verified during comparison of AM and PM X-ray datasets, we can conclude that odontological identity was established. All above-mentioned discrepancies could be classified as explainable. Inter-agreement rate between two investigators considering correspondence between AM and PM datasets reached Cohen's kappa level which is equal to 0,97, while positive 100% agreement was reached considering 21 out of 24 analyzed characteristics.
conclusionAvailable AM and PM radiographical datasets represent a sufficient information for effective forensic dental identification, even if such were obtained by different roentgenological techniques (orthopantomography and periapical radiography). Using of an adapted algorithm for digital images comparison with forensic dental purposes could potentially overcome cognitive bias and observer's effect, speed up the process of analysis and increase the accuracy and inter-agreement rate while referencing AM and PM datasets.
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