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Background <p>Age estimation is vital in forensic science, with maxillary sinus development serving as a reliable indicator. This study developed an automatic segmentation model for maxillary sinus identification and parameter measurement, combined with regression and machine learning models for age estimation.</p> Methods <p>Cone Beam Computed Tomography (CBCT) images from 292 Han individuals (ranging from 5 to 53 years) were used to train and validate the segmentation model. Measurements included sinus dimensions (length, width, height), inter-sinus distance, and volume. Age estimation models using multiple linear regression and random forest algorithms were built based on these variables.</p> Results <p>The automatic segmentation model achieved high accuracy, which yielded a Dice similarity coefficient (DSC) of 0.873, an Intersection over Union (IoU) of 0.7753, a Hausdorff Distance 95% (HD95) of 9.8337, and an Average Surface Distance (ASD) of 2.4507. The regression model performed best, with mean absolute errors (MAE) of 1.45 years (under 18) and 3.51 years (aged 18 and above), providing relatively precise age predictions.</p> Conclusion <p>The maxillary sinus-based model is a promising tool for age estimation, particularly in adults, and could be enhanced by incorporating additional variables like dental dimensions.</p>

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Automatic maxillary sinus segmentation and age estimation model for the northwestern Chinese Han population

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月26日

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  • Yu-Xin Guo,
  • Jun-Long Lan,
  • Wen-Qing Bu,
  • Yu Tang,
  • Di Wu,
  • Hui Yang,
  • Jia-Chen Ren,
  • Yu-Xuan Song,
  • Hong-Ying Yue,
  • Yu-Cheng Guo,
  • Hao-Tian Meng

摘要

Background

Age estimation is vital in forensic science, with maxillary sinus development serving as a reliable indicator. This study developed an automatic segmentation model for maxillary sinus identification and parameter measurement, combined with regression and machine learning models for age estimation.

Methods

Cone Beam Computed Tomography (CBCT) images from 292 Han individuals (ranging from 5 to 53 years) were used to train and validate the segmentation model. Measurements included sinus dimensions (length, width, height), inter-sinus distance, and volume. Age estimation models using multiple linear regression and random forest algorithms were built based on these variables.

Results

The automatic segmentation model achieved high accuracy, which yielded a Dice similarity coefficient (DSC) of 0.873, an Intersection over Union (IoU) of 0.7753, a Hausdorff Distance 95% (HD95) of 9.8337, and an Average Surface Distance (ASD) of 2.4507. The regression model performed best, with mean absolute errors (MAE) of 1.45 years (under 18) and 3.51 years (aged 18 and above), providing relatively precise age predictions.

Conclusion

The maxillary sinus-based model is a promising tool for age estimation, particularly in adults, and could be enhanced by incorporating additional variables like dental dimensions.