Lee Jae-Hong, Kim Young-Taek, Lee Jong-Bin, Jeong Seong-Nyum
Department of Periodontology, Daejeon Dental Hospital, Institute of Wonkwang Dental Research, Wonkwang University College of Dentistry, Daejeon, Korea.
Department of Periodontology, National Health Insurance Service Ilsan Hospital, Goyang, Korea.
J Periodontal Implant Sci. 2022 Jun;52(3):220-229. doi: 10.5051/jpis.2104080204.
The aim of this study was to evaluate and compare the accuracy performance of dental professionals in the classification of different types of dental implant systems (DISs) using panoramic radiographic images with and without the assistance of a deep learning (DL) algorithm.
Using a self-reported questionnaire, the classification accuracy of dental professionals (including 5 board-certified periodontists, 8 periodontology residents, and 31 dentists not specialized in implantology working at 3 dental hospitals) with and without the assistance of an automated DL algorithm were determined and compared. The accuracy, sensitivity, specificity, confusion matrix, receiver operating characteristic (ROC) curves, and area under the ROC curves were calculated to evaluate the classification performance of the DL algorithm and dental professionals.
Using the DL algorithm led to a statistically significant improvement in the average classification accuracy of DISs (mean accuracy: 78.88%) compared to that without the assistance of the DL algorithm (mean accuracy: 63.13%, <0.05). In particular, when assisted by the DL algorithm, board-certified periodontists (mean accuracy: 88.56%) showed higher average accuracy than did the DL algorithm, and dentists not specialized in implantology (mean accuracy: 77.83%) showed the largest improvement, reaching an average accuracy similar to that of the algorithm (mean accuracy: 80.56%).
The automated DL algorithm classified DISs with accuracy and performance comparable to those of board-certified periodontists, and it may be useful for dental professionals for the classification of various types of DISs encountered in clinical practice.
本研究旨在评估和比较牙科专业人员在有无深度学习（DL）算法辅助下，使用全景X线影像对不同类型牙种植系统（DISs）进行分类的准确性表现。
通过一份自填式问卷，确定并比较了牙科专业人员（包括3家牙科医院的5名获得委员会认证的牙周病专家、8名牙周病住院医师和31名非种植专业牙医）在有无自动DL算法辅助下的分类准确性。计算准确性、敏感性、特异性、混淆矩阵、受试者工作特征（ROC）曲线以及ROC曲线下面积，以评估DL算法和牙科专业人员的分类表现。
与无DL算法辅助相比，使用DL算法使DISs的平均分类准确性有统计学显著提高（平均准确性：78.88%）（无DL算法辅助时平均准确性：63.13%，P<0.05）。特别是在DL算法辅助下，获得委员会认证的牙周病专家（平均准确性：88.56%）的平均准确性高于DL算法，非种植专业牙医（平均准确性：77.83%）的提高幅度最大，达到了与算法相近的平均准确性（平均准确性：80.56%）。
自动DL算法对DISs的分类准确性和性能与获得委员会认证的牙周病专家相当，可能有助于牙科专业人员对临床实践中遇到的各种类型DISs进行分类。
