Graduate Program in Health Sciences, Federal University of Sergipe, Rua Claudio Batista s/n, 49060-100, Aracaju, Brazil; Investigative Pathology Laboratory, Federal University of Sergipe, Rua Claudio Batista s/n, 49060-100, Aracaju, Brazil.
Department of Nursing, Federal University of Sergipe, Rua Claudio Batista s/n, 49060-100, Aracaju, Brazil.
J Forensic Leg Med. 2024 Aug;106:102734. doi: 10.1016/j.jflm.2024.102734. Epub 2024 Jul 30.
Age estimation is crucial in legal and humanitarian contexts. Forensic professionals may use various procedures to estimate age, including dental analysis, bone density tests, evaluation of physical characteristics including facial bone structure and development, and image-based methods. Although images are often the only material available, visual observation of photographic material is an imprecise method in age estimation, which can compromise judicial decision-making. Analyzing 4000 photographs from the Brazilian Federal Police database, representing four age groups (6, 10, 14, and 18 years), the study employed automated analysis by marking 28 photogrammetric points. Data were used to establish facial patterns by age and sex using the facial geometric morphometrics method. Performance was assessed through a Multinomial Logistic Regression model, evaluating accuracy, sensitivity, and specificity across the categorical age groups. Analyses were conducted using R software, with a 5 % significance level. The study found that facial geometric morphometrics achieved an overall accuracy of 69.3 % in age discrimination, with higher accuracy in males (74.7 %) compared to females (65.8 %) (p < 0.001). The method excelled at predicting the age of 6-year-olds with 87.3 % sensitivity and 95.6 % specificity but had lower performance at 14 years. It showed greater accuracy in distinguishing age groups with larger age gaps, achieving up to 99.5 % accuracy between certain groups, and was particularly effective in differentiating ages of 6 and 10 years in females and 10, 14, and 18 years in males. The facial geometric morphometrics emerges as a promising approach for age estimation among children and adolescents in forensic settings.
年龄估算是法律和人道主义领域的关键。法医专业人员可能会使用各种程序来估计年龄，包括牙齿分析、骨密度测试、身体特征评估，包括面部骨骼结构和发育，以及基于图像的方法。尽管图像通常是唯一可用的材料，但仅凭视觉观察照片材料进行年龄估计是一种不精确的方法，可能会影响司法决策。本研究分析了来自巴西联邦警察局数据库的 4000 张照片，代表四个年龄组（6、10、14 和 18 岁），使用自动化分析标记了 28 个摄影测量点。该研究使用面部几何形态计量学方法，通过按年龄和性别标记 28 个摄影测量点，对数据进行分析，建立面部模式。通过多项逻辑回归模型评估性能，在分类年龄组中评估准确性、敏感性和特异性。分析使用 R 软件进行，显著性水平为 5%。研究发现，面部几何形态计量学在年龄判别方面的总体准确率为 69.3%，男性（74.7%）的准确率高于女性（65.8%）（p<0.001）。该方法在预测 6 岁儿童的年龄方面表现出色，敏感性为 87.3%，特异性为 95.6%，但在 14 岁时表现较差。它在区分年龄差距较大的年龄组方面表现出色，在某些组之间达到了高达 99.5%的准确率，并且在区分女性 6 岁和 10 岁、男性 10 岁、14 岁和 18 岁的年龄方面特别有效。面部几何形态计量学在法医环境中对儿童和青少年的年龄估计是一种很有前途的方法。
