Institute of Pathology, Laboratory and Forensic Medicine (I-PPerForM), Universiti Teknologi MARA, Sungai Buloh Campus, Sungai Buloh, Selangor, Malaysia; Faculty of Dentistry, Universiti Teknologi MARA, Sungai Buloh Campus, Sungai Buloh, Selangor, Malaysia.
Faculty of Dentistry, Universiti Teknologi MARA, Sungai Buloh Campus, Sungai Buloh, Selangor, Malaysia.
Forensic Sci Int. 2024 Aug;361:112150. doi: 10.1016/j.forsciint.2024.112150. Epub 2024 Jul 15.
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.
当灾难发生时，当局必须优先考虑两件事。首先是抢救生命，其次是确认和处理死者。然而，在大规模灾难中，需要对成千上万的尸体进行个体识别，法医团队面临着一些挑战，例如长时间工作导致鉴定过程延迟，以及尸体分解引起的公共卫生问题。利用牙科全景成像，牙齿已被用于法医学作为个体年龄的物理标记。传统上，牙齿年龄的估计是由专家手动进行的。虽然这个过程相当简单，但在大规模灾难中，由于受害者数量众多，以及在有限的时间内完成评估的压力，法医工作变得更加具有挑战性。人工智能（AI）在医学和牙科领域的出现，使得自动化当前流程成为传统方法的替代方案。本研究旨在测试开发的深度卷积神经网络系统在使用数字牙科全景成像的大型马来西亚儿童外样本数据集上进行年龄估计的准确性和性能。法医牙科估计实验室（F-DentEst Lab）是一款计算机应用程序，用于进行数字化的牙科年龄估计。引入这个系统是为了改进传统的年龄估计方法，基于人工智能方法显著提高年龄估计过程的效率。总共回顾性地收集了 1892 张数字牙科全景图像来测试 F-DentEst Lab。在 F-DentEst Lab 的开发早期阶段进行了数据训练、验证和测试，其中分配涉及 80%的训练和剩余 20%的测试。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，使用动态规划主动轮廓（DP-AC）方法和深度卷积神经网络（DCNN）分别对下颌前磨牙进行分割和分类，以及统计分析。建议的 DCNN 方法低估了实际年龄，女性和男性的平均绝对误差（ME）分别为 0.03 和 0.05。
