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<p>Accurate adult age estimation remains a central challenge in forensic anthropology. This study compared the performance of the conventional Suchey-Brooks (SB) method with a deep learning (DL) approach in adult age estimation using standardized cinematic volume rendering (cVR) from pelvic CT scans. A total of 1, 359 examinations from a Chinese cohort were analyzed. SB-based cubic regression achieved mean absolute errors (MAE) of 5.94 years in males and 6.06 years in females, while the DL model produced MAEs of 6.64 and 7.03 years, respectively. No significant accuracy difference was observed between methods. Both exhibited age-dependent bias, with overestimation in younger adults and underestimation in older individuals. Visualization using gradient-weighted class activation mapping indicated that the DL model focused on key morphological features of the pubic symphyseal surface, with patterns varying across age groups and sex. These results demonstrate that both cVR-based SB phase assignment and deep learning regression achieve compatible precision, with deep learning providing a scalable, automated, first-line approach for objective age estimation.</p>

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Age estimation from pubic symphysis based on cinematic volume rendering: comparison between Suchey-Brooks staging and deep learning

  • 期刊论文

  • 发表日期：

    2026年6月27日

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  • Yu-chi Zhou,
  • Shuai Luo,
  • Meng Liu,
  • Nian-zu Lv,
  • Li-rong Qiu,
  • Fei Fan,
  • Lei Shi,
  • Yong Liu,
  • Hu Chen,
  • Zhen‑hua Deng,
  • Meng‑jun Zhan

摘要

Accurate adult age estimation remains a central challenge in forensic anthropology. This study compared the performance of the conventional Suchey-Brooks (SB) method with a deep learning (DL) approach in adult age estimation using standardized cinematic volume rendering (cVR) from pelvic CT scans. A total of 1, 359 examinations from a Chinese cohort were analyzed. SB-based cubic regression achieved mean absolute errors (MAE) of 5.94 years in males and 6.06 years in females, while the DL model produced MAEs of 6.64 and 7.03 years, respectively. No significant accuracy difference was observed between methods. Both exhibited age-dependent bias, with overestimation in younger adults and underestimation in older individuals. Visualization using gradient-weighted class activation mapping indicated that the DL model focused on key morphological features of the pubic symphyseal surface, with patterns varying across age groups and sex. These results demonstrate that both cVR-based SB phase assignment and deep learning regression achieve compatible precision, with deep learning providing a scalable, automated, first-line approach for objective age estimation.