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Objective <p>This study aimed to evaluate the feasibility and accuracy of an artificial intelligence (AI)-based method for automatically measuring femoral length from postmortem computed tomography (PMCT) images and to develop regression models for estimating stature in Japanese individuals.</p> Materials and methods <p>PMCT data from 163 deceased individuals aged 20–73 years were examined using an AI-based segmentation tool. Semantic segmentation was performed using the TotalSegmentator library, and the maximum femoral length was estimated using the Double Sweep method. Measurement reproducibility was assessed using intraclass correlation coefficients (ICC). The adjusted stature (AS) was calculated by subtracting 2.0&#xa0;cm from the stature measured during the autopsy to estimate living stature. The correlations between femoral measurements and AS were analyzed using simple linear regression. After deriving the stature estimation formulae from the regression analyses, the time required from the initiation of femoral measurement to the display of the estimated stature was recorded.</p> Results <p>Femoral measurements demonstrated excellent reproducibility, with ICC values &gt; 0.999 and no outliers detected. Significant positive correlations were observed between femoral measurements and AS across all models (<i>p</i> &lt; 0.001). The adjusted coefficients of determination values exceeded 0.80 for the total sample, and the root mean square error values were 3.5&#xa0;cm or less. The mean time from the initiation of femoral measurement to the display of the estimated stature was 23.05&#xa0;s.</p> Conclusion <p>AI-driven femoral measurement from PMCT images provides a highly accurate, efficient, and reproducible approach for forensic stature estimation.</p> Clinical trial number <p>Not applicable.</p>

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Automated femoral measurement from postmortem computed tomographic images using artificial intelligence for forensic stature estimation in a Japanese population

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月22日

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  • Suguru Torimitsu,
  • Hisahiro Ikari,
  • Shigeki Tsuneya,
  • Yukiko Uemura,
  • Mio Okada,
  • Masatoshi Kojima,
  • Fumiko Chiba,
  • Hirotaro Iwase,
  • Eiryo Kawakami,
  • Yohsuke Makino

摘要

Objective

This study aimed to evaluate the feasibility and accuracy of an artificial intelligence (AI)-based method for automatically measuring femoral length from postmortem computed tomography (PMCT) images and to develop regression models for estimating stature in Japanese individuals.

Materials and methods

PMCT data from 163 deceased individuals aged 20–73 years were examined using an AI-based segmentation tool. Semantic segmentation was performed using the TotalSegmentator library, and the maximum femoral length was estimated using the Double Sweep method. Measurement reproducibility was assessed using intraclass correlation coefficients (ICC). The adjusted stature (AS) was calculated by subtracting 2.0 cm from the stature measured during the autopsy to estimate living stature. The correlations between femoral measurements and AS were analyzed using simple linear regression. After deriving the stature estimation formulae from the regression analyses, the time required from the initiation of femoral measurement to the display of the estimated stature was recorded.

Results

Femoral measurements demonstrated excellent reproducibility, with ICC values > 0.999 and no outliers detected. Significant positive correlations were observed between femoral measurements and AS across all models (p < 0.001). The adjusted coefficients of determination values exceeded 0.80 for the total sample, and the root mean square error values were 3.5 cm or less. The mean time from the initiation of femoral measurement to the display of the estimated stature was 23.05 s.

Conclusion

AI-driven femoral measurement from PMCT images provides a highly accurate, efficient, and reproducible approach for forensic stature estimation.

Clinical trial number

Not applicable.