Kim Yu-Rin, Choi Jae-Hyeok, Ko Jihyeong, Jung Young-Jin, Kim Byeongjun, Nam Seoul-Hee, Chang Won-Du
Department of Dental Hygiene, Silla University, 140 Baegyang-daero 700 Beon-gil, Sasang-gu, Busan 46958, Republic of Korea.
Department of Artificial Intelligence, Pukyong National University, Busan 48513, Republic of Korea.
Healthcare (Basel). 2023 Apr 8;11(8):1068. doi: 10.3390/healthcare11081068.
Automatic age estimation using panoramic dental radiographic images is an important procedure for forensics and personal oral healthcare. The accuracies of the age estimation have increased recently with the advances in deep neural networks (DNN), but DNN requires large sizes of the labeled dataset which is not always available. This study examined whether a deep neural network is able to estimate tooth ages when precise age information is not given. A deep neural network model was developed and applied to age estimation using an image augmentation technique. A total of 10,023 original images were classified according to age groups (in decades, from the 10s to the 70s). The proposed model was validated using a 10-fold cross-validation technique for precise evaluation, and the accuracies of the predicted tooth ages were calculated by varying the tolerance. The accuracies were 53.846% with a tolerance of ±5 years, 95.121% with ±15 years, and 99.581% with ±25 years, which means the probability for the estimation error to be larger than one age group is 0.419%. The results indicate that artificial intelligence has potential not only in the forensic aspect but also in the clinical aspect of oral care.
使用全景牙科X光图像进行自动年龄估计是法医学和个人口腔保健中的一项重要程序。随着深度神经网络（DNN）的发展，年龄估计的准确性最近有所提高，但DNN需要大量的标记数据集，而这些数据集并非总是可用。本研究探讨了在没有精确年龄信息的情况下，深度神经网络是否能够估计牙齿年龄。开发了一种深度神经网络模型，并使用图像增强技术将其应用于年龄估计。根据年龄组（按十年划分，从10岁到70岁）对总共10,023张原始图像进行了分类。使用10折交叉验证技术对所提出的模型进行验证以进行精确评估，并通过改变容差来计算预测牙齿年龄的准确性。容差为±5岁时准确率为53.846%，±15岁时为95.121%，±25岁时为99.581%，这意味着估计误差大于一个年龄组的概率为0.419%。结果表明，人工智能不仅在法医学方面，而且在口腔护理的临床方面都具有潜力。
