Koch Rahel Mara, Mentzel Hans-Joachim, Heinrich Andreas
Department of Radiology, Jena University Hospital-Friedrich Schiller University, Am Klinikum 1, 07747, Jena, Germany.
Section of Pediatric Radiology, Department of Radiology, Jena University Hospital-Friedrich Schiller University, Am Klinikum 1, 07747, Jena, Germany.
Eur Radiol. 2025 Jan 25. doi: 10.1007/s00330-025-11373-y.
Forensic age estimation from orthopantomograms (OPGs) can be performed more quickly and accurately using convolutional neural networks (CNNs), making them an ideal extension to standard forensic age estimation methods. This study evaluates improvements in forensic age prediction for children, adolescents, and young adults by training a custom CNN from a previous study, using a larger, diverse dataset with a focus on dental growth features.
21,814 OPGs from 13,766 individuals aged 1 to under 25 years were utilized. The custom CNN underwent 1000 epochs of training and validation using 16,000 and 4000 OPGs, respectively. The best model was chosen by the least mean absolute error (MAE) and evaluated with an additional test dataset of 1814 independent OPGs. Furthermore, the CNN was applied to OPGs from 15 available forensic age estimations conducted by experts certified by the Study Group on Forensic Age Diagnostics (AGFAD), and the results were compared.
A MAE of 0.93 ± 0.81 years and a mean-signed error (MSE) of -0.06 ± 1.23 years were achieved in the test dataset. 63% of predictions were accurate within 1 year, and 95% within 2.5 years. Results of the CNN were comparable to those obtained by experts, effectively highlighting discrepancies in the reported ages of individuals.
Using a large and diverse dataset along with custom deep learning techniques, forensic age estimation can be significantly improved, often providing predictions accurate to within 1 year. This approach offers a reliable, robust, and objective complement to standard forensic age estimation methods.
Question The potential of custom convolutional neural networks for forensic age estimation, along with a large, diverse dataset, warrants further investigation, offering valuable support to experts. Findings For 1814 test-orthopantomograms, 63% of predictions were accurate within 1 year and 95% within 2.5 years, similar to expert estimates in 15 forensic cases. Clinical relevance Many individuals' fates depend on accurate age estimation. Forensic age estimation can benefit from applying CNN-based methods to further enhance reliability and accuracy.
使用卷积神经网络（CNN）可以更快、更准确地从曲面断层片（OPG）进行法医年龄估计，使其成为标准法医年龄估计方法的理想扩展。本研究通过在前一项研究的基础上训练一个定制的CNN，使用更大、更多样化的数据集并侧重于牙齿生长特征，来评估儿童、青少年和年轻成年人法医年龄预测的改进情况。
利用了来自13766名年龄在1岁至25岁以下个体的21814张OPG。定制的CNN分别使用16000张和4000张OPG进行了1000个 epoch的训练和验证。通过最小平均绝对误差（MAE）选择最佳模型，并使用1814张独立OPG的额外测试数据集进行评估。此外，将该CNN应用于由法医年龄诊断研究小组（AGFAD）认证的专家进行的15例可用法医年龄估计的OPG，并比较结果。
测试数据集中实现了0.93±0.81岁的MAE和-0.06±1.23岁的平均符号误差（MSE）。63%的预测在1年内准确，95%在2.5年内准确。CNN的结果与专家获得的结果相当，有效地突出了个体报告年龄中的差异。
使用大量多样的数据集以及定制的深度学习技术，可以显著改进法医年龄估计，通常能提供精确到1年内的预测。这种方法为标准法医年龄估计方法提供了可靠、稳健和客观的补充。
问题 定制卷积神经网络在法医年龄估计方面的潜力，以及大量多样的数据集，值得进一步研究，为专家提供有价值的支持。发现 对于1814张测试曲面断层片，63%的预测在1年内准确，95%在2.5年内准确，与15例法医案件中的专家估计相似。临床意义 许多人的命运取决于准确的年龄估计。法医年龄估计可以受益于应用基于CNN的方法来进一步提高可靠性和准确性。
