Author:
Li Hairui,Li Haizhen,Yuan Lingjun,Liu Chao,Xiao Shengzhao,Liu Zhen,Zhou Guoli,Dong Ting,Ouyang Ningjuan,Liu Lu,Ma Chenglong,Feng Yang,Zheng Youyi,Xia Lunguo,Fang Bing
Abstract
Abstract
Background
Many scholars have proven cervical vertebral maturation (CVM) method can predict the growth and development and assist in choosing the best time for treatment. However, assessing CVM is a complex process. The experience and seniority of the clinicians have an enormous impact on judgment. This study aims to establish a fully automated, high-accuracy CVM assessment system called the psc-CVM assessment system, based on deep learning, to provide valuable reference information for the growth period determination.
Methods
This study used 10,200 lateral cephalograms as the data set (7111 in train set, 1544 in validation set and 1545 in test set) to train the system. The psc-CVM assessment system is designed as three parts with different roles, each operating in a specific order. 1) Position Network for locating the position of cervical vertebrae; 2) Shape Recognition Network for recognizing and extracting the shapes of cervical vertebrae; and 3) CVM Assessment Network for assessing CVM according to the shapes of cervical vertebrae. Statistical analysis was conducted to detect the performance of the system and the agreement of CVM assessment between the system and the expert panel. Heat maps were analyzed to understand better what the system had learned. The area of the third (C3), fourth (C4) cervical vertebrae and the lower edge of second (C2) cervical vertebrae were activated when the system was assessing the images.
Results
The system has achieved good performance for CVM assessment with an average AUC (the area under the curve) of 0.94 and total accuracy of 70.42%, as evaluated on the test set. The Cohen's Kappa between the system and the expert panel is 0.645. The weighted Kappa between the system and the expert panel is 0.844. The overall ICC between the psc-CVM assessment system and the expert panel was 0.946. The F1 score rank for the psc-CVM assessment system was: CVS (cervical vertebral maturation stage) 6 > CVS1 > CVS4 > CVS5 > CVS3 > CVS2.
Conclusions
The results showed that the psc-CVM assessment system achieved high accuracy in CVM assessment. The system in this study was significantly consistent with expert panels in CVM assessment, indicating that the system can be used as an efficient, accurate, and stable diagnostic aid to provide a clinical aid for determining growth and developmental stages by CVM.
Publisher
Springer Science and Business Media LLC
Reference32 articles.
1. Bjork A. Variations in the growth pattern of the human mandible: longitudinal radiographic study by the implant method. J Dent Res. 1963: 42(1)Pt 2:400–411.
2. Hunter CJ. The correlation of facial growth with body height and skeletal maturation at adolescence. Angle Orthod. 1966;36(1):44–54.
3. Hanna AE, Sanjad S, Andary R, Nemer G, Ghafari JG. Tooth development associated with mutations in hereditary vitamin D-resistant rickets. JDR Clin Trans Res. 2018;3(1):28–34.
4. Haffner D, Emma F, Eastwood DM, Duplan MB, Bacchetta J, Schnabel D, Wicart P, Bockenhauer D, Santos F, Levtchenko E, et al. Clinical practice recommendations for the diagnosis and management of X-linked hypophosphataemia. Nat Rev Nephrol. 2019;15(7):435–55.
5. Nik Kantaputra P, Jotikasthira D, Carlson B, Wongmaneerung T, Quarto N, Khankasikum T, Powcharoen W, Intachai W, Tripuwabhrut K. TRPS1 mutation associated with trichorhinophalangeal syndrome type 1 with 15 supernumerary teeth, hypoplastic mandibular condyles with slender condylar necks and unique hair morphology. J Dermatol. 2020;47(7):774–8.
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献