Kim Dogun, Choi Jaeho, Ahn Sangyoon, Park Eunil
Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul, Republic of Korea.
Department of Dental Biomaterials Science, Dental Research Institute, Seoul National University, Seoul, Republic of Korea.
J Ambient Intell Humaniz Comput. 2023;14(2):1123-1131. doi: 10.1007/s12652-021-03366-8. Epub 2021 Jul 6.
In this study, a home dental care system consisting of an oral image acquisition device and deep learning models for maxillary and mandibular teeth images is proposed. The presented method not only classifies tooth diseases, but also determines whether a professional dental treatment (NPDT) is required. Additionally, a specially designed oral image acquisition device was developed to perform image acquisition of maxillary and mandibular teeth. Two evaluation metrics, namely, tooth disease and NPDT classifications, were examined using 610 compounded and 5251 tooth images annotated by an experienced dentist with a Doctor of Dental Surgery and another dentist with a Doctor of Dental Medicine. In the tooth disease and NPDT classifications, the proposed system showed accuracies greater than 96% and 89%, respectively. Based on these results, we believe that the proposed system will allow users to effectively manage their dental health by detecting tooth diseases by providing information on the need for dental treatment.
The online version contains supplementary material available at 10.1007/s12652-021-03366-8.
在本研究中,提出了一种由口腔图像采集设备以及用于上颌和下颌牙齿图像的深度学习模型组成的家庭牙齿护理系统。所提出的方法不仅能对牙齿疾病进行分类,还能确定是否需要专业的牙科治疗(NPDT)。此外,还开发了一种专门设计的口腔图像采集设备,用于对上颌和下颌牙齿进行图像采集。使用由一位有牙科外科学博士学位的经验丰富的牙医和另一位有牙科医学博士学位的牙医标注的610张合成牙齿图像和5251张真实牙齿图像,对两种评估指标,即牙齿疾病分类和NPDT分类进行了检验。在牙齿疾病分类和NPDT分类中,所提出的系统分别显示出大于96%和89%的准确率。基于这些结果,我们相信所提出的系统将通过提供牙科治疗需求信息来检测牙齿疾病,从而帮助用户有效地管理他们的牙齿健康。
在线版本包含可在10.1007/s12652-021-03366-8获取的补充材料。