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<p>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. Twenty-one CNNs and two Transformer-based architectures were tested. Tasks consisted of binary sex and age (≤ 15 vs. &gt;15 years) and multiclass (sex + age) classifications. For sex classification, the highest accuracies were achieved by DeiT (0.807), ViT (0.806), and EfficientNetV2M (0.781), while for age classification, YOLOv11 (0.953), ViT (0.949), and DeiT (0.946) showed the best performance. The multiclass task yielded accuracies of 0.754, 0.753 and 0.734 by YOLOv11, DeiT, and ViT, respectively. Transformers consistently outperformed conventional CNNs, while YOLOv11 and EfficientNetV2M also demonstrated competitive performance. The studied artificial intelligence models may be useful as adjuncts for binary sex and age classification, but multiclass applications are still premature needing further research before their use in forensic practice can be recommended.</p>

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Maxillary sinus classification for sex and age using 23 artificial intelligence architectures

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月19日

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  • Wahaj Anees,
  • Rianne Silva,
  • Amber Khan,
  • Jared Murray,
  • Leonardo Scavassini,
  • Mariana Burle,
  • Nikolaos Angelakopoulos,
  • Marcelo Henrique Napimoga,
  • Lucas Porto,
  • André Abade,
  • Ademir Franco

摘要

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. Twenty-one CNNs and two Transformer-based architectures were tested. Tasks consisted of binary sex and age (≤ 15 vs. >15 years) and multiclass (sex + age) classifications. For sex classification, the highest accuracies were achieved by DeiT (0.807), ViT (0.806), and EfficientNetV2M (0.781), while for age classification, YOLOv11 (0.953), ViT (0.949), and DeiT (0.946) showed the best performance. The multiclass task yielded accuracies of 0.754, 0.753 and 0.734 by YOLOv11, DeiT, and ViT, respectively. Transformers consistently outperformed conventional CNNs, while YOLOv11 and EfficientNetV2M also demonstrated competitive performance. The studied artificial intelligence models may be useful as adjuncts for binary sex and age classification, but multiclass applications are still premature needing further research before their use in forensic practice can be recommended.