Development of a deep-learning algorithm for age estimation on CT images of the vertebral column

Kawashita Ikuo

Fukumoto Wataru

Mitani Hidenori

Narita Keigo

Chosa Keigo

Nakamura Yuko

Nagao Masataka

Awai Kazuo

Legal Medicine

Purpose
The accurate age estimation of cadavers is essential for their identification. However, conventional methods fail to yield adequate age estimation especially in elderly cadavers. We developed a deep learning algorithm for age estimation on CT images of the vertebral column and checked its accuracy.
Method
For the development of our deep learning algorithm, we included 1,120 CT data of the vertebral column of 140 patients for each of 8 age decades. The deep learning model of regression analysis based on Visual Geometry Group-16 (VGG16) was improved in its estimation accuracy by bagging. To verify its accuracy, we applied our deep learning algorithm to estimate the age of 219 cadavers who had undergone postmortem CT (PMCT). The mean difference and the mean absolute error (MAE), the standard error of the estimate (SEE) between the known- and the estimated age, were calculated. Correlation analysis using the intraclass correlation coefficient (ICC) and Bland-Altman analysis were performed to assess differences between the known- and the estimated age.
Results
For the 219 cadavers, the mean difference between the known- and the estimated age was 0.30 years; it was 4.36 years for the MAE, and 5.48 years for the SEE. The ICC (2,1) was 0.96 (95 % confidence interval: 0.95–0.97, p < 0.001). Bland-Altman analysis showed that there were no proportional or fixed errors (p = 0.08 and 0.41).
Conclusions
Our deep learning algorithm for estimating the age of 219 cadavers on CT images of the vertebral column was more accurate than conventional methods and highly useful.

Cadaver

Deep learning

CT

Spine

英語

Elsevier

© 2024. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/

This is not the published version. Please cite only the published version.

この論文は出版社版ではありません。引用の際には出版社版をご確認、ご利用ください。

[DOI] https://doi.org/10.1016/j.legalmed.2024.102444 ～の異版である