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Abstract Studies have relied on conventional imaging and traditional morphometric analyses of the maxillary sinuses (MS) for sex and age estimation, but little is known about the performance of deep learning models. This study aimed to evaluate the diagnostic accuracy of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) in classifying individuals by sex and age through the radiographic assessment of the MS. Panoramic radiographs of individuals aged 6–22.99 years were sampled.
Abstract Deep learning tools based on computer vision have emerged as alternative methods for assessing radiographic image patterns. These approaches have been explored for various forensic applications, including sex and age estimation. This study aimed to evaluate the diagnostic accuracy of a Convolutional Neural Network (CNN) in classifying radiographic images by sex and age, focusing on the nasal aperture as the morphological feature of interest.
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
