Application of Artificaial Intelligence in Stomatology
Automated diagnostic classification with lateral cephalograms based on deep learning network model
Chang Qiao, Wang Shaofeng, Zuo Feifei, Wang Fan, Gong Beiwen, Wang Yajie, Xie Xianju
Published 2023-06-09
Cite as Chin J Stomatol, 2023, 58(6): 547-553. DOI: 10.3760/cma.j.cn112144-20230305-00072
Abstract
ObjectiveTo establish a comprehensive diagnostic classification model of lateral cephalograms based on artificial intelligence (AI) to provide reference for orthodontic diagnosis.
MethodsA 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.
ResultsThe 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.
ConclusionsIn 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.
Key words:
Artificial intelligence; Diagnosis, computer-assisted; Cephalometry; Orthodontics; Diagnosis; Deep learning
Contributor Information
Chang Qiao
Department of Orthodontics, Capital Medical University School of Stomatology, Beijing 100050, China
Wang Shaofeng
Department of Orthodontics, Capital Medical University School of Stomatology, Beijing 100050, China
Zuo Feifei
LargeV Instrument Corp., Ltd, Beijing 100084, China
Wang Fan
Department of Orthodontics, Capital Medical University School of Stomatology, Beijing 100050, China
Gong Beiwen
Department of Orthodontics, Capital Medical University School of Stomatology, Beijing 100050, China
Wang Yajie
LargeV Instrument Corp., Ltd, Beijing 100084, China
Xie Xianju
Department of Orthodontics, Capital Medical University School of Stomatology, Beijing 100050, China
诊断分类。Kim等［11］证实，与自动定点推导分类相比，直接图像分类具有一定优势。目前头颅侧位X线片的直接图像分类研究主要针对矢状及垂直骨面型分类，未能单独分析上颌与下颌的骨性特征，而牙性分类仅包含覆
，影响其临床进一步应用。本项研究拟通过开源DenseNet121网络构建头颅侧位X线片自动诊断分类模型，扩展骨性及牙性诊断分类，以期为临床提供参考。
