Automated diagnostic classification with lateral cephalograms based on deep learning network model.
10.3760/cma.j.cn112144-20230305-00072
- Author:
Qiao CHANG
1
;
Shao Feng WANG
1
;
Fei Fei ZUO
2
;
Fan WANG
1
;
Bei Wen GONG
1
;
Ya Jie WANG
2
;
Xian Ju XIE
1
Author Information
1. Department of Orthodontics, Capital Medical University School of Stomatology, Beijing 100050, China.
2. LargeV Instrument Corp., Ltd, Beijing 100084, China.
- Publication Type:Journal Article
- MeSH:
Male;
Female;
Humans;
Young Adult;
Adult;
Artificial Intelligence;
Deep Learning;
Cephalometry;
Maxilla;
Mandible/diagnostic imaging*
- From:
Chinese Journal of Stomatology
2023;58(6):547-553
- CountryChina
- Language:Chinese
-
Abstract:
Objective: 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.
