Choi Hye-Ran, Siadari Thomhert Suprapto, Kim Jo-Eun, Huh Kyung-Hoe, Yi Won-Jin, Lee Sam-Sun, Heo Min-Suk
Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, Republic of Korea.
Artificial Intelligence Research Centre, Digital Dental Hub Incorporation, Seoul, Republic of Korea.
Forensic Sci Res. 2022 Mar 9;7(3):456-466. doi: 10.1080/20961790.2022.2034714. eCollection 2022.
Disaster victim identification issues are especially critical and urgent after a large-scale disaster. The aim of this study was to suggest an automatic detection of natural teeth and dental treatment patterns based on dental panoramic radiographs (DPRs) using deep learning to promote its applicability as human identifiers. A total of 1 638 DPRs, of which the chronological age ranged from 20 to 49 years old, were collected from January 2000 to November 2020. This dataset consisted of natural teeth, prostheses, teeth with root canal treatment, and implants. The detection of natural teeth and dental treatment patterns including the identification of teeth number was done with a pre-trained object detection network which was a convolutional neural network modified by EfficientDet-D3. The objective metrics for the average precision were 99.1% for natural teeth, 80.6% for prostheses, 81.2% for treated root canals, and 96.8% for implants, respectively. The values for the average recall were 99.6%, 84.3%, 89.2%, and 98.1%, in the same order, respectively. This study showed outstanding performance of convolutional neural network using dental panoramic radiographs in automatically identifying teeth number and detecting natural teeth, prostheses, treated root canals, and implants.
在大规模灾难后，灾难受害者身份识别问题尤为关键和紧迫。本研究的目的是基于牙科全景X光片（DPR），利用深度学习技术实现自然牙齿和牙科治疗模式的自动检测，以提高其作为人类身份识别标识的适用性。从2000年1月至2020年11月，共收集了1638张DPR，其年龄范围在20至49岁之间。该数据集包括自然牙齿、假牙、根管治疗后的牙齿和种植牙。自然牙齿和牙科治疗模式的检测，包括牙齿编号的识别，是通过一个预训练的目标检测网络完成的，该网络是由EfficientDet-D3修改的卷积神经网络。自然牙齿、假牙、根管治疗后的牙齿和种植牙的平均精度客观指标分别为99.1%、80.6%、81.2%和96.8%。平均召回率的值依次分别为99.6%、84.3%、89.2%和98.1%。本研究表明，利用牙科全景X光片的卷积神经网络在自动识别牙齿编号以及检测自然牙齿、假牙、根管治疗后的牙齿和种植牙方面表现出色。
