Irurita Olivares Javier, Gámez-Granados Juan Carlos, Rubio Salvador Ángel, García Reina Ana, Gutiérrez Pascual Emma, Castillo Jiménez Laura, Damas Arroyo Sergio, Cordón García Oscar, Alemán Aguilera Inmaculada
Department of Legal Medicine, Toxicology, and Physical Anthropology. School of Medicine, University of Granada, Granada, Spain.
Department of Electronic and Computer Engineering, University of Córdoba, Córdoba, Spain.
Int J Legal Med. 2025 Jun 4. doi: 10.1007/s00414-025-03511-4.
Traditional age estimation methods based on macroscopic observation has been criticized for being excessively dependent on the observer's experience. The aim of this technical note is to propose a new atlas to assist the forensic practitioner in labelling pubic symphysis components. Furthermore, intra- and inter-observer evaluation was conducted using both novice and experienced practitioners. Two experienced and two novice practitioners have used this atlas to label 1,127 identified pubes from autopsies. Furthermore, they have considered the phases of Todd's method (1920) to estimate the age of each pubis. A previously published, semi-automatic artificial intelligence rule-based method based on the C4.5 algorithm has also been used to recommend a specific age-at-death estimation from the human-defined labels, to be compared with the macroscopic age estimation performed by all observers. Linear weighted kappa coefficients indicate that the intra- and inter-observer error when using the new atlas is higher for novice practitioners (Kappa < 0,6) than for experienced practitioners (Kappa > 0,6). Component labeling produces less error than phase assignment following the traditional method only in the case of experienced practitioners. In addition, the artificial intelligence method achieves a global percentage of correct estimates similar to what the four practitioners can achieve. The proposed atlas can be thus considered an effective tool for component labeling. Besides, explainable machine learning techniques could help automate age estimation methods through component analysis. These techniques reduce subjectivity, but it is important that researchers engage in the process to ensure the replicability of the method. Nevertheless, these results must be regarded as preliminary until they are subjected to a more extensive evaluation by a larger cohort of observers.
基于宏观观察的传统年龄估计方法因过度依赖观察者的经验而受到批评。本技术说明的目的是提出一本新图谱，以协助法医从业者标记耻骨联合的组成部分。此外，还使用新手和经验丰富的从业者进行了观察者内部和观察者之间的评估。两名经验丰富的从业者和两名新手从业者使用这本图谱对1127例尸检中确定的耻骨进行了标记。此外，他们还参考了托德方法（1920年）的阶段来估计每块耻骨的年龄。还使用了一种先前发表的基于C4.5算法的半自动人工智能规则方法，从人工定义的标签中推荐特定的死亡年龄估计值，以便与所有观察者进行的宏观年龄估计相比较。线性加权kappa系数表明，新手从业者使用新图谱时观察者内部和观察者之间的误差（Kappa＜0.6）高于经验丰富的从业者（Kappa＞0.6）。只有在经验丰富的从业者中，组件标记产生的误差才比传统方法的阶段分配产生的误差小。此外，人工智能方法实现的正确估计的总体百分比与四位从业者能够实现的百分比相似。因此，所提出的图谱可被视为组件标记的有效工具。此外，可解释的机器学习技术有助于通过组件分析使年龄估计方法自动化。这些技术减少了主观性，但研究人员参与这一过程以确保方法的可重复性很重要。然而，在由更多观察者进行更广泛的评估之前，这些结果必须被视为初步结果。
