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http://dx.doi.org/10.5933/JKAPD.2022.49.1.85

Comparative Validation of the Mixed and Permanent Dentition at Web-Based Artificial Intelligence Cephalometric Analysis  

Shin, Sunhahn (Division of Pediatric Dentistry, Department of Dentistry, Ewha Womans University Mokdong Hospital)
Kim, Donghyun (Division of Pediatric Dentistry, Department of Dentistry, Ewha Womans University Mokdong Hospital)
Publication Information
Journal of the korean academy of Pediatric Dentistry / v.49, no.1, 2022 , pp. 85-94 More about this Journal
Abstract
This retrospective study aimed to evaluate the difference in measurement between conventional orthodontic analysis and artificial intelligence orthodontic analysis in pediatric and adolescent patients aged 7 - 15 with the mixed and permanent dentition. A total of 60 pediatric and adolescent patients (30 mixed dentition, 30 permanent dentition) who underwent lateral cephalometric radiograph for orthodontic diagnosis were randomly selected. Seventeen cephalometric landmarks were identified, and 22 measurements were calculated by 1 examiner, using both conventional analysis method and deep learning-based analysis method. Errors due to repeated measurements were assessed by Pearson's correlation coefficient. For the mixed dentition group and the permanent dentition group, respectively, a paired t-test was used to evaluate the difference between the 2 methods. The difference between the 2 methods for 8 measurements were statistically significant in mixed dentition group: APDI, SNA, SNB, Mandibular plane angle, LAFH (p < 0.001), Facial ratio (p = 0.001), U1 to SN (p = 0.012), and U1 to A-Pg (p = 0.021). In the permanent dentition group, 4 measurements showed a statistically significant difference between the 2 methods: ODI (p = 0.020), Wits appraisal (p = 0.025), Facial ratio (p = 0.026), and U1 to A-Pg (p = 0.001). Compared with the time-consuming conventional orthodontic analysis, the deep learning-based cephalometric system can be clinically acceptable in terms of reliability and validity. However, it is essential to understand the limitations of the deep learning-based programs for orthodontic analysis of pediatric and adolescent patients and use these programs with the proper assessment.
Keywords
Cephalometric radiography; Deep learning; Artificial intelligence; Orthodontic diagnosis;
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1 Nishimoto S, Sotsuka Y, Kakibuchi M, et al. : Personal computer-based cephalometric landmark detection with deep learning, using cephalograms on the internet. J Craniofac Surg, 30:91-95, 2019.   DOI
2 Arik SO, Ibragimov B, Xing L : Fully automated quantitative cephalometry using convolutional neural networks. J Med Imaging, 4:014501, 2017.   DOI
3 Santoro M, Jarjoura K, Cangialosi TJ : Accuracy of digital and analogue cephalometric measurements assessed with the sandwich technique. Am J Orthod Dentofacial Orthop, 129:345-351, 2006.   DOI
4 Broadbent BH : A new X-ray technique and its application to orthodontia: the introduction of cephalometric radiography. Angle Orthod, 51:93-114, 1981.
5 Kim YH : Web based and artificial intelligence driven orthodontic analysis system. J Clin Digit Dent, 1:24-28, 2019.
6 Albarakati S, Kula K, Ghoneima A : The reliability and reproducibility of cephalometric measurements: a comparison of conventional and digital methods. Dentomaxillofac Radiol, 41:11-17, 2012.   DOI
7 Naoumova J, Lindman R : A comparison of manual traced images and corresponding scanned radiographs digitally traced. Eur J Orthod, 31:247-253, 2009.   DOI
8 Sayinsu K, Isik F, Trakyali G, Arun T : An evaluation of the errors in cephalometric measurements on scanned cephalometric images and conventional tracings. Eur J Orthod, 29:105-108, 2007.   DOI
9 Lim KF, Foong K : Phosphor-stimulated computed cephalometry: reliability of landmark identification. Br J Orthod, 24:301-308, 1997.   DOI
10 Sekiguchi T, Savara BS : Variability of cephalometric landmarks used for face growth studies. Am J Orthod, 61:603-618, 1972.   DOI
11 Hwang HW, Moon JH, Lee SJ, et al. : Evaluation of automated cephalometric analysis based on the latest deep learning method. Angle Orthod, 91:329-335, 2021.   DOI
12 Kim YH, Park JB, Jung SK, et al. : Influence of the Depth of the Convolutional Neural Networks on an Artificial Intelligence Model for Diagnosis of Orthognathic Surgery. J Pers Med, 11:356-366, 2021.   DOI
13 Seki K, Okano T : Exposure reduction in cephalography with a digital photostimulable phosphor imaging system. Dentomaxillofac Radiol, 22:127-130, 1993.   DOI
14 Lee KS, Jung SK, Choi J, et al. : Evaluation of transfer learning with deep convolutional neural networks for screening osteoporosis in dental panoramic radiographs. J Clin Med, 9:392-404, 2020.   DOI
15 Gravely J, Benzies PM : The clinical significance of tracing error in cephalometry. Br J Orthod, 1:95-101, 1974.   DOI
16 Chen YJ, Chen SK, Chang HF, Chen KC : Comparison of landmark identification in traditional versus computer-aided digital cephalometry. Angle Orthod, 70:387-392, 2000.
17 Baumrind S, Frantz RC : The reliability of head film measurements: 2. Conventional angular and linear measures. Am J Orthod, 60:505-517, 1971.   DOI
18 Cohen A, Linney A : A Low Cost System for Computer Based Cephalometric Analysis. Br J Orthod, 13:105-108, 1986.   DOI
19 Kim JY, Kwon JY, Kim KH, Park KT : Study on lateral cphalogram of children with normal occlusion in the primary dentition. J Korean Acad Pediatr Dent, 32:649-656, 2005.
20 Baumrind S, Frantz RC : The reliability of head film measurements: 1. Landmark identification. Am J Orthod, 60:111-127, 1971.   DOI
21 Alqahtani H : Evaluation of an online website-based platform for cephalometric analysis. J Stomatol Oral Maxillofac Surg, 121:53-57, 2020.   DOI
22 Jung SK, Kim TW : New approach for the diagnosis of extractions with neural network machine learning. Am J Orthod Dentofacial Orthop, 149:127-133, 2016.   DOI
23 Chi J, Walia E, Eramian M, et al. : Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network. J Digit Imaging, 30:477-486, 2017.   DOI