Ozlu Ucan Gulfem, Gwassi Omar Abboosh Hussein, Apaydin Burak Kerem, Ucan Bahadir
Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Istanbul Gelisim University, Istanbul 34310, Turkey.
Electrical and Computer Engineering, School of Engineering and Natural Sciences, Altinbas University, Istanbul 34217, Turkey.
Diagnostics (Basel). 2025 Jan 29;15(3):314. doi: 10.3390/diagnostics15030314.
Dental age estimation is a vital component of forensic science, helping to determine the identity and actual age of an individual. However, its effectiveness is challenged by methodological variability and biological differences between individuals. Therefore, to overcome the drawbacks such as the dependence on manual measurements, requiring a lot of time and effort, and the difficulty of routine clinical application due to large sample sizes, we aimed to automatically estimate tooth age from panoramic radiographs (OPGs) using artificial intelligence (AI) algorithms. Two-Dimensional Deep Convolutional Neural Network (2D-DCNN) and One-Dimensional Deep Convolutional Neural Network (1D-DCNN) techniques were used to extract features from panoramic radiographs and patient records. To perform age estimation using feature information, Genetic algorithm (GA) and Random Forest algorithm (RF) were modified, combined, and defined as Modified Genetic-Random Forest Algorithm (MG-RF). The performance of the system used in our study was analyzed based on the MSE, MAE, RMSE, and R values calculated during the implementation of the code. As a result of the applied algorithms, the MSE value was 0.00027, MAE value was 0.0079, RMSE was 0.0888, and R score was 0.999. The findings of our study indicate that the AI-based system employed herein is an effective tool for age detection. Consequently, we propose that this technology could be utilized in forensic sciences in the future.
牙齿年龄估计是法医学的一个重要组成部分，有助于确定个体的身份和实际年龄。然而，其有效性受到方法学变异性和个体间生物学差异的挑战。因此，为了克服诸如依赖手动测量、需要大量时间和精力以及由于样本量大而难以进行常规临床应用等缺点，我们旨在使用人工智能（AI）算法从全景X线片（OPG）中自动估计牙齿年龄。使用二维深度卷积神经网络（2D-DCNN）和一维深度卷积神经网络（1D-DCNN）技术从全景X线片和患者记录中提取特征。为了使用特征信息进行年龄估计，对遗传算法（GA）和随机森林算法（RF）进行修改、组合，并定义为改进遗传-随机森林算法（MG-RF）。基于代码实现过程中计算的均方误差（MSE）、平均绝对误差（MAE）、均方根误差（RMSE）和R值，分析了我们研究中使用的系统的性能。应用算法的结果是，MSE值为0.00027，MAE值为0.0079，RMSE为0.0888，R分数为0.999。我们研究的结果表明，本文采用的基于人工智能的系统是一种有效的年龄检测工具。因此，我们建议该技术未来可用于法医学。
