Sex estimation on panoramic dental radiographs: A methodological approach

Estimating sex using tooth radiographs requires knowledge of a comprehensive spectrum of maxillar anatomy, which ultimately demands specialization on the anatomical structures in the oral cavity. In this paper, we propose a more effective methodological study than others present in the literature for the problem of automatic sex estimation. Our methodology uses the largest publicly available data set in the literature, raises statistical significance in the performance assessment, and explains which part of the images influences the classification. Our findings showed that although EfficientNetV2-Large reached an average F1-score of 91,43% +- 0,67, an EfficientNet-B0 could be more beneficial with a very close F1-score and a much lighter architecture.