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

## TL;DR

This study compares 23 AI models for estimating sex and age from maxillary sinus radiographs, finding that Vision Transformers and YOLOv11 perform best.

## Contribution

The study introduces a comprehensive evaluation of 23 AI architectures for sex and age classification using maxillary sinus radiographs.

## Key findings

- Vision Transformers (ViT) and DeiT achieved top accuracy in sex classification.
- YOLOv11 and ViT showed the best performance in age classification.
- Transformers outperformed traditional CNNs in most tasks.

## 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. 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.

The online version contains supplementary material available at 10.1038/s41598-026-36112-1.