The significance of evaluating sphenoid sinus fluid by postmortem computed tomography in cases of drowning

Prompt personal identification is required during disasters that can result in many casualties. To rapidly estimate sex based on skull structure, this study applied deep learning using two-dimensional silhouette images, obtained from head postmortem computed tomography (PMCT), to enhance the outline shape of the skull. We investigated the process of sex estimation using silhouette images viewed from different angles and majority votes. A total of 264 PMCT cases (132 cases for each sex) were used for transfer learning with two deep-learning models (AlexNet and VGG16). VGG16 exhibited the highest accuracy (89.8%) for lateral projections. The accuracy improved to 91.7% when implementing a majority vote based on the results of multiple projection angles. Moreover, silhouette images can be obtained from simple and popular X-ray imaging in addition to PMCT. Thus, this study demonstrated the feasibility of sex estimation by combining silhouette images with deep learning. The results implied that X-ray images can be used for personal identification.

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Enhancing CT Image Quality Using Conditional Generative Adversarial Networks for Applying Post-mortem Computed Tomography in Forensic Pathology: A Phantom Study

Yoon,

Heo,

Kim

et al. 2023

J. Radiol. Sci. Technol.

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The significance of evaluating sphenoid sinus fluid by postmortem computed tomography in cases of drowning

Cited by 5 publications

References 19 publications

Diagnosing fatal drownings: A review of the postmortem findings

Diagnosing fatal drownings: A review of the postmortem findings

Sex estimation using skull silhouette images from postmortem computed tomography by deep learning

Enhancing CT Image Quality Using Conditional Generative Adversarial Networks for Applying Post-mortem Computed Tomography in Forensic Pathology: A Phantom Study

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