Tanikawa Chihiro, Lee Chonho, Lim Jaeyoen, Oka Ayaka, Yamashiro Takashi
Graduate School of Dentistry, Osaka University, Suita, Japan.
Center for Advanced Medical Engineering and Informatics, Osaka University, Suita, Japan.
Orthod Craniofac Res. 2021 Dec;24 Suppl 2:43-52. doi: 10.1111/ocr.12501. Epub 2021 Jun 4.
To determine whether AI systems that recognize cephalometric landmarks can apply to various patient groups and to examine the patient-related factors associated with identification errors.
The present retrospective cohort study analysed digital lateral cephalograms obtained from 1785 Japanese orthodontic patients. Patients were categorized into eight subgroups according to dental age, cleft lip and/or palate, orthodontic appliance use and overjet.
An AI system that automatically recognizes anatomic landmarks on lateral cephalograms was used. Thirty cephalograms in each subgroup were randomly selected and used to test the system's performance. The remaining cephalograms were used for system learning. The success rates in landmark recognition were evaluated using confidence ellipses with α = 0.99 for each landmark. The selection of test samples, learning of the system and evaluation of the system were repeated five times for each subgroup. The mean success rate and identification error were calculated. Factors associated with identification errors were examined using a multiple linear regression model.
The success rate and error varied among subgroups, ranging from 85% to 91% and 1.32 mm to 1.50 mm, respectively. Cleft lip and/or palate was found to be a factor associated with greater identification errors, whereas dental age, orthodontic appliances and overjet were not significant factors (all, P < .05).
Artificial intelligence systems that recognize cephalometric landmarks could be applied to various patient groups. Patient-oriented errors were found in patients with cleft lip and/or palate.
确定识别头影测量标志点的人工智能系统是否可应用于不同患者群体，并检查与识别错误相关的患者相关因素。
本回顾性队列研究分析了从1785名日本正畸患者获得的数字化侧位头影测量片。根据牙龄、唇腭裂、正畸矫治器使用情况和覆盖情况将患者分为八个亚组。
使用一个能自动识别侧位头影测量片上解剖标志点的人工智能系统。每个亚组随机选取30张头影测量片用于测试系统性能。其余头影测量片用于系统学习。使用α = 0.99的置信椭圆评估每个标志点的识别成功率。每个亚组对测试样本的选择、系统学习和系统评估重复进行五次。计算平均成功率和识别误差。使用多元线性回归模型检查与识别误差相关的因素。
各亚组的成功率和误差各不相同，分别为85%至91%和1.32毫米至1.50毫米。发现唇腭裂是与更大识别误差相关的一个因素，而牙龄、正畸矫治器和覆盖情况不是显著因素（均P < 0.05）。
识别头影测量标志点的人工智能系统可应用于不同患者群体。在唇腭裂患者中发现了以患者为导向的误差。
