Abstract：
When a disaster occurs, the authority must prioritise two things. First, the search and rescue of lives, and second, the identification and management of deceased individuals. However, with thousands of dead bodies to be individually identified in mass disasters, forensic teams face challenges such as long working hours resulting in a delayed identification process and a public health concern caused by the decomposition of the body. Using dental panoramic imaging, teeth have been used in forensics as a physical marker to estimate the age of an individual. Traditionally, dental age estimation has been performed manually by experts. Although the procedure is fairly simple, the large number of victims and the limited amount of time available to complete the assessment during large-scale disasters make forensic work even more challenging. The emergence of artificial intelligence (AI) in the fields of medicine and dentistry has led to the suggestion of automating the current process as an alternative to the conventional method. This study aims to test the accuracy and performance of the developed deep convolutional neural network system for age estimation in large, out-of-sample Malaysian children dataset using digital dental panoramic imaging. Forensic Dental Estimation Lab (F-DentEst Lab) is a computer application developed to perform the dental age estimation digitally. The introduction of this system is to improve the conventional method of age estimation that significantly increase the efficiency of the age estimation process based on the AI approach. A total number of one-thousand-eight-hundred-and-ninety-two digital dental panoramic images were retrospectively collected to test the F-DentEst Lab. Data training, validation, and testing have been conducted in the early stage of the development of F-DentEst Lab, where the allocation involved 80 % training and the remaining 20 % for testing. The methodology was comprised of four major steps: image preprocessing, which adheres to the inclusion criteria for panoramic dental imaging, segmentation, and classification of mandibular premolars using the Dynamic Programming-Active Contour (DP-AC) method and Deep Convolutional Neural Network (DCNN), respectively, and statistical analysis. The suggested DCNN approach underestimated chronological age with a small ME of 0.03 and 0.05 for females and males, respectively.
摘要：
当灾难发生时,当局必须优先考虑两件事。首先,搜索和营救生命，第二,死者的身份识别和管理。然而,在大规模灾难中，成千上万的尸体被单独识别，法医小组面临挑战，例如工作时间长，导致身份识别过程延迟，以及身体分解引起的公共卫生问题。使用牙科全景成像，在法医中，牙齿已被用作估计个体年龄的物理标记。传统上,牙科年龄估计由专家手动进行。虽然程序相当简单,在大规模灾难期间，受害者人数众多，完成评估的时间有限，这使得法医工作更具挑战性。人工智能（AI）在医学和牙科领域的出现导致建议将当前过程自动化，以替代传统方法。本研究旨在测试开发的深度卷积神经网络系统的准确性和性能，用于年龄估计，使用数字牙科全景成像的样本外马来西亚儿童数据集。法医牙科估计实验室（F-DentEst实验室）是一种计算机应用程序，旨在以数字方式进行牙科年龄估计。该系统的引入是为了改进传统的年龄估计方法，从而显着提高基于AI方法的年龄估计过程的效率。回顾性收集了总共一千八百九十二张数字牙科全景图像，以测试F-DentEst实验室。数据训练,验证,并且在F-DentEst实验室开发的早期阶段进行了测试，其中分配涉及80％的培训，其余20％用于测试。该方法包括四个主要步骤：图像预处理，符合全景牙科成像的纳入标准，分割，使用动态规划主动轮廓（DP-AC）方法和深度卷积神经网络（DCNN）对下颌前磨牙进行分类，分别,和统计分析。建议的DCNN方法低估了实际年龄，女性和男性的ME分别为0.03和0.05，分别。
