Liang Xiaohong, Wang Chudong, Wen Dan, Tian Zhikai, Zhang Yike, Hou Lihua, Chen Bingxu, Wu Wenshuang, Wang Yali, Zha Lagabaiyila, Liu Ying
Department of Oral Implantology, Xiangya Stomatological Hospital, Central South University, No. 72 Xiangya Road, Kaifu District, Changsha, Hunan Province, PR China.
Department of Forensic Science, School of Basic Medical Sciences, Central South University, No. 172 Tongzipo Road, Changsha, Hunan Province, 410013, PR China.
Int J Legal Med. 2025 Sep 3. doi: 10.1007/s00414-025-03591-2.
Age inference is a key focus of forensic work, and traditional dental age inference methods require individuals to have a complete dental arch. However, congenital or acquired tooth loss may lead to random tooth loss in individuals, resulting in bias in age prediction. To address this issue, we validated and modified Bedek's tooth age inference method (a method for inferring the age of a population with missing teeth) for the first time in the Chinese population of children with complete dentition, congenital tooth loss, and acquired tooth loss, and constructed two new machine learning based tooth age inference methods (unilateral mandible and bilateral mandible tooth age estimation models) in this population. The unilateral mandible model was constructed using the remaining five teeth of the left mandible, excluding the lateral incisor and the second premolar of congenital tooth loss, and the first premolars and first molars of the acquired tooth loss, to estimate chronological age (the two most common types of missing teeth in the Chinese population, respectively). However, the actual types of missing teeth in the population are varied, and the information on the location of missing teeth is often replaced by the developmental morphology of the contralateral teeth. In order to augment the predictive information available to model, we further constructed a bilateral mandible model containing 14 individual mandibular teeth by filling in missing values using datawig. In the male agenesis validation group, the MAE values of the best bilateral, unilateral mandible model, and modified Bedek model were 0.641, 0.715, and 0.920, respectively. In females, the MAE values were 0.763, 0.785, and 0.990, respectively. In the male acquired tooth loss validation group, the MAE values of the three models were 0.793, 0.728, and 1.376, respectively. In females, the MAE values were 0.744, 0.779, and 1.094, respectively. Collectively, these novel odontological age-estimation frameworks provide robust, flexible solutions for forensic casework involving partial dentitions. By accommodating variable patterns of congenital and acquired tooth loss without sacrificing predictive precision, they constitute a critical advancement in the forensic identification of unknown or disputed-age individuals.
年龄推断是法医工作的一个关键重点，传统的牙齿年龄推断方法要求个体具有完整的牙弓。然而，先天性或后天性牙齿缺失可能导致个体牙齿随机缺失，从而导致年龄预测出现偏差。为了解决这个问题，我们首次在中国有完整牙列、先天性牙齿缺失和后天性牙齿缺失的儿童群体中验证并修改了贝德克牙齿年龄推断方法（一种推断有牙齿缺失人群年龄的方法），并在该群体中构建了两种基于机器学习的新的牙齿年龄推断方法（单侧下颌骨和双侧下颌骨牙齿年龄估计模型）。单侧下颌骨模型是使用左下颌骨剩余的五颗牙齿构建的，不包括先天性牙齿缺失的侧切牙和第二前磨牙，以及后天性牙齿缺失的第一前磨牙和第一磨牙，以估计实足年龄（这分别是中国人群中两种最常见的牙齿缺失类型）。然而，人群中实际的牙齿缺失类型各不相同，牙齿缺失位置的信息往往被对侧牙齿的发育形态所取代。为了增加模型可用的预测信息，我们使用datawig填充缺失值，进一步构建了一个包含14颗个体下颌牙齿的双侧下颌骨模型。在男性牙齿发育不全验证组中，最佳双侧模型、单侧下颌骨模型和改良贝德克模型的平均绝对误差（MAE）值分别为0.641、0.715和0.920。在女性中，MAE值分别为0.763、0.785和0.990。在男性后天性牙齿缺失验证组中，这三种模型的MAE值分别为0.793、0.728和1.376。在女性中，MAE值分别为0.744、0.779和1.094。总体而言，这些新的牙科学年龄估计框架为涉及部分牙列的法医案件提供了强大、灵活的解决方案。通过适应先天性和后天性牙齿缺失的可变模式而不牺牲预测精度，它们在法医鉴定未知或有争议年龄的个体方面构成了一项关键进展。
