摘要目的:基于深度学习构建头颅侧位X线片自动诊断分类模型，为正畸临床诊断提供参考。方法:收集2015年1月至2021年12月就诊于首都医科大学口腔医学院正畸科的正畸患者头颅侧位X线片2 894张，构建数据集，包括1 351例男性和1 543例女性，年龄（26.4±7.4）岁。先由1名正畸专业主治医师和1名博士研究生（正畸工作年限分别为8和5年）进行人工定点，测量头影测量项目并进行初分类，再由1名正畸专业主任医师和1名主治医师（正畸工作年限均超过20年）进行核查，内容包含8项骨性和牙性诊断分类。数据按7∶2∶1的比例分别纳入训练集、验证集和测试集。使用开源DenseNet121网络（一种深度学习模型）构建头颅侧位X线片自动诊断分类模型。模型训练后，使用测试集计算模型的分类准确性、精确性、敏感性、特异性，输出受试者工作曲线并计算曲线下面积评估模型性能；输出热力图，可视化模型关注区域。结果:成功构建头颅侧位X线片自动诊断分类模型，其对1张头颅侧位X线片作出8项诊断分类平均需要0.112 s。其中5项诊断分类的准确性为80%~90%，包括矢状骨面型、下颌发育、垂直骨面型、上前牙倾斜情况和下前牙突出情况；3项诊断分类的准确性为70%~80%，包括上颌发育、下前牙倾斜情况、上前牙突出情况。各项诊断分类的总体95%可信区间曲线下面积均≥0.90。热力图显示，分类成功的头颅侧位X线片的激活区域分布于分类相关结构区域。结论:本项研究基于DenseNet121网络构建了头颅侧位X线片自动诊断分类模型，其可实现8项临床常用诊断项目的快速分类。

abstractsObjective:To establish a comprehensive diagnostic classification model of lateral cephalograms based on artificial intelligence (AI) to provide reference for orthodontic diagnosis.Methods:A total of 2 894 lateral cephalograms were collected in Department of Orthodontics, Capital Medical University School of Stomatology from January 2015 to December 2021 to construct a data set, including 1 351 males and 1 543 females with a mean age of (26.4± 7.4) years. Firstly, 2 orthodontists (with 5 and 8 years of orthodontic experience, respectively) performed manual annotation and calculated measurement for primary classification, and then 2 senior orthodontists (with more than 20 years of orthodontic experience) verified the 8 diagnostic classifications including skeletal and dental indices. The data were randomly divided into training, validation, and test sets in the ratio of 7∶2∶1. The open source DenseNet121 was used to construct the model. The performance of the model was evaluated by classification accuracy, precision rate, sensitivity, specificity and area under the curve (AUC). Visualization of model regions of interest through class activation heatmaps.Results:The automatic classification model of lateral cephalograms was successfully established. It took 0.012 s on average to make 8 diagnoses on a lateral cephalogram. The accuracy of 5 classifications was 80%-90%, including sagittal and vertical skeletal facial pattern, mandibular growth, inclination of upper incisors, and protrusion of lower incisors. The acuracy rate of 3 classifications was 70%-80%, including maxillary growth, inclination of lower incisors and protrusion of upper incisors. The average AUC of each classification was ≥0.90. The class activation heat map of successfully classified lateral cephalograms showed that the AI model activation regions were distributed in the relevant structural regions.Conclusions:In this study, an automatic classification model for lateral cephalograms was established based on the DenseNet121 to achieve rapid classification of eight commonly used clinical diagnostic items.