Estimating Biological Gender from Panoramic Dental X-Ray Images

Identifying the gender of a person is one of the fundamental tasks in forensic medicine. One possible application is right after a catastrophic event such as a mass disaster with a high victim count. In such cases it is necessary to identify the people involved which can require a high number of forensic experts, depending on the scale of the event. With panoramic dental x-ray images the biological gender of a person can be estimated by analyzing skeletal structures that express sexual dimorphism. Current methods require the manual measurement of a wide array of mandibular parameters which are then manually compared to references based on these measurements and assumed ethnicity of the people involved. We propose an automated solution based on deep learning techniques using convolutional neural networks. Our data consists of 4000 panoramic dental x-ray images of patients with European origin, with the images being taken by a wide range of orthopantomographs. Our automated method can estimate 64 images per second on contemporary hardware, it doesn't require human intervention for estimation and it achieves state-of-the-art results with an accuracy of 96.87% ± 0.96%.