Sex estimation from maxillofacial radiographs using a deep learning approach.

Journal: Dental materials journal
PMID: 38599831

Abstract

The purpose of this study was to construct deep learning models for more efficient and reliable sex estimation. Two deep learning models, VGG16 and DenseNet-121, were used in this retrospective study. In total, 600 lateral cephalograms were analyzed. A saliency map was generated by gradient-weighted class activation mapping for each output. The two deep learning models achieved high values in each performance metric according to accuracy, sensitivity (recall), precision, F1 score, and areas under the receiver operating characteristic curve. Both models showed substantial differences in the positions indicated in saliency maps for male and female images. The positions in saliency maps also differed between VGG16 and DenseNet-121, regardless of sex. This analysis of our proposed system suggested that sex estimation from lateral cephalograms can be achieved with high accuracy using deep learning.

Authors

  • Hiroki Hase
    Department of Medical Systems Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University.
  • Yuichi Mine
    Department of Medical System Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
  • Shota Okazaki
    Department of Medical System Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
  • Yuki Yoshimi
    Department of Orthodontics and Craniofacial Developmental Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
  • Shota Ito
    Department of Orthodontics and Craniofacial Developmental Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
  • Tzu-Yu Peng
    School of Dentistry, College of Dentistry, China Medical University, Taichung, 404, Taiwan.
  • Mizuho Sano
    Department of Medical Systems Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University.
  • Yuma Koizumi
    Department of Orthodontics and Craniofacial Developmental Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University.
  • Naoya Kakimoto
    Department of Oral and Maxillofacial Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
  • Kotaro Tanimoto
    Department of Orthodontics and Craniofacial Developmental Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
  • Takeshi Murayama
    Department of Medical System Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.

Keywords

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