Department of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Jodhpur, India, 342005.
School of Forensic Sciences, National Forensic Sciences University, Tripura, India, 799001.
Anthropol Anz. 2024 Jun 3;81(3):301-314. doi: 10.1127/anthranz/2023/1723.
Within the pelvis the iliac crest and ischial tuberosity display delayed ossification and fusion, thus, presenting as reliable maturity indicators. Amongst the different iliac crest and ischial tuberosity age estimation methods, the modified Kreitner-Kellinghaus stages constitute one of the more promising methods. The present study was directed towards establishing the applicability of the modified Kreitner-Kellinghaus method using five supervised machine learning approaches. Clinical CT scans of consenting individuals were collected and scored using the modified Kreitner-Kellinghaus method for the iliac crest and ischial tuberosity, independently. Age was subsequently estimated using different machine learning models. Cumulative scores computed from both markers were additionally employed for age estimation using machine learning. For iliac crest age estimation, Random Forest and Gradient Boosting Regression furnished lowest mean absolute error (2.42 years) and root mean square error (3.06 years). For ischial tuberosity age estimation, Gradient Boosting Regression garnered the lowest computations of mean absolute error (2.60 years) and root mean square error (3.09 years). For cumulative score based age estimation, Support Vector Regression and Gradient Boosting Regression yielded lowest mean absolute error (2.48 years) and root mean square error (3.07 years). Obtained error computations indicate that the iliac crest is a more accurate age marker in comparison to the ischial tuberosity. Additionally, cumulative score-based approaches garnered similar/ marginally more precise results in comparison to the iliac crest with all five models. This marginal improvement is not sufficient to justify employing the relatively more complicated cumulative score-based approach for age estimation. Hence, whenever available, the iliac crest should be preferred over the ischial tuberosity/ cumulative score-based approaches for age estimation.
在骨盆内，髂嵴和坐骨结节显示出延迟的骨化和融合，因此成为可靠的成熟度指标。在不同的髂嵴和坐骨结节年龄估计方法中，改良的 Kreitner-Kellinghaus 分期是较有前途的方法之一。本研究旨在使用五种监督机器学习方法来确定改良的 Kreitner-Kellinghaus 方法的适用性。收集了同意的个体的临床 CT 扫描，并使用改良的 Kreitner-Kellinghaus 方法对髂嵴和坐骨结节进行了评分，分别进行了评分。随后使用不同的机器学习模型估计年龄。从两个标志物计算的累积分数还用于使用机器学习进行年龄估计。对于髂嵴年龄估计，随机森林和梯度提升回归提供了最低的平均绝对误差（2.42 年）和均方根误差（3.06 年）。对于坐骨结节年龄估计，梯度提升回归获得了最低的平均绝对误差（2.60 年）和均方根误差（3.09 年）。对于基于累积分数的年龄估计，支持向量回归和梯度提升回归产生了最低的平均绝对误差（2.48 年）和均方根误差（3.07 年）。获得的误差计算表明，与坐骨结节相比，髂嵴是更准确的年龄标志物。此外，与髂嵴相比，所有五个模型的基于累积分数的方法都获得了相似/略有更精确的结果。这种边际改善不足以证明采用相对复杂的基于累积分数的方法进行年龄估计是合理的。因此，只要有条件，就应优先选择髂嵴而不是坐骨结节/基于累积分数的方法进行年龄估计。
