Marcelo Henrique Napimoga

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.

Loading metrics Open Access Peer-reviewed Research Article Victor Angelo Martins Montalli , Aguinaldo Silva Garcez , Laís Viana Canuto de Oliveira , Marcelo Sperandio , Marcelo Henrique Napimoga , Rogério Heládio Lopes Motta Victor Angelo Martins Montalli, Aguinaldo Silva Garcez, Laís Viana Canuto de Oliveira, Marcelo Sperandio, Marcelo Henrique Napimoga, Rogério Heládio Lopes Motta Published: February 18, 2021 https://doi.org/10.1371/journal.pone.0247029 Figures Abstract Strategies to return...