Department of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Jodhpur, India.
School of Forensic Sciences, National Forensic Sciences University, Tripura, India.
Med Sci Law. 2024 Jul;64(3):204-216. doi: 10.1177/00258024231198917. Epub 2023 Sep 5.
Age estimation constitutes an integral parameter of identification. In children, sub-adults, and young adults, accurate age estimation is vital on various aspects of civil, criminal, and immigration law. The iliac crest presents as a suitable age marker within these age cohorts, and the modified Risser method constitutes a relatively novel and unexplored method for iliac crest age estimation. The present study attempted to ascertain the applicability of this modified method for age estimation in the Indian population, an aspect previously unexplored, through computed tomographic examination of the iliac crest. Computed tomography scans of consenting individuals undergoing routine examinations of the pelvis/ abdomen for various clinically indicated reasons were collected and scored using the modified Risser stages. Computed tomographic examinations of the iliac crest indicate that the recalibrated method accurately depicts the temporal progression of ossification and fusion changes. Different regression and machine learning models were subsequently derived and/or trained to evaluate the accuracy and precision associated with the method. Amongst the ten regression models derived herein, compound regression exhibited the lowest inaccuracy (4.78 years) and root mean squared error values (5.46 years). Machine learning yielded further reduced error rates, with decision tree regression achieving inaccuracy and root mean squared error values of 1.88 years and 2.28 years, respectively. A comparative evaluation of error computations obtained from regression analysis and machine learning illustrates the statistical superiority of machine learning for forensic age estimation. Error computations obtained with machine learning suggest that the modified Risser method is capable of permitting reliable age estimation within criminal and civil proceedings.
年龄估算是身份鉴定的一个重要参数。在儿童、青少年和年轻成年人中，准确的年龄估算是民法、刑法和移民法等各个方面的关键。髂嵴在这些年龄组中是一个合适的年龄标志物，改良的 Risser 法是一种相对新颖且尚未探索的髂嵴年龄估测方法。本研究试图通过髂嵴的计算机断层扫描来确定这种改良方法在印度人群中的适用性，这是一个以前未被探索的方面。通过对因各种临床原因接受骨盆/腹部常规检查的同意个体进行计算机断层扫描，并使用改良的 Risser 分期进行评分。髂嵴的计算机断层扫描表明，经校准的方法可以准确描绘出骨化和融合变化的时间进程。随后衍生和/或训练了不同的回归和机器学习模型，以评估该方法的准确性和精密度。在本文中推导的十个回归模型中，复合回归表现出最低的不准确性（4.78 年）和均方根误差值（5.46 年）。机器学习进一步降低了误差率，决策树回归的不准确性和均方根误差值分别达到 1.88 年和 2.28 年。回归分析和机器学习的误差计算的比较评估表明，机器学习在法医年龄估测方面具有统计学优势。机器学习获得的误差计算表明，改良的 Risser 法能够在刑事和民事程序中进行可靠的年龄估计。
