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혼합치열과 영구치열 환자를 대상으로 한 웹 기반 인공지능 두부 계측 분석에서의 비교 검증

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

  • 신선한 (이화여자대학교 의과대학 목동병원 소아치과학교실) ;
  • 김동현 (이화여자대학교 의과대학 목동병원 소아치과학교실)
  • 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)
  • 투고 : 2021.08.13
  • 심사 : 2021.11.03
  • 발행 : 2022.02.28

초록

이 후향적 연구의 목적은 7 - 15세 사이의 혼합치열기와 영구치열기의 소아 및 청소년 환자에서 기존 교정 분석 방법과 인공 지능을 활용한 교정 분석 방법을 이용한 변수의 차이를 비교하여 평가하는 것이다. 교정 진단을 위해 측면 두부계측 방사선 사진을 촬영한 소아 환자 60명(혼합 치열기 30명, 영구치열기 30명)을 무작위로 선정하였다. V-ceph을 사용한 기존 분석 방법과 WebCeph를 사용한 딥 러닝 기반 분석 방법으로 1명의 검사자가 17개의 두부 측정 계측점을 식별하고, 22개의 측정 항목을 평가했다. 기존 분석 방법의 반복 측정으로 인한 오차는 Pearson의 상관 분석을 사용하여 평가하었다. 혼합치열군과 영구치열군에 대한 각각 두 방법의 차이는 paired t-test를 사용하여 평가하였다. 혼합치열군에서 두 분석 방법의 차이는 8개의 계측항목에서 통계적으로 유의하였다: 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). 영구치열군에서는 두 분석 방법 간에 4개의 계측항목이 통계적으로 유의한 차이를 보였다: ODI (p = 0.020), Wits appraisal (p = 0.025), Facial ratio (p = 0.026), and U1 to A-Pg (p = 0.001). 많은 시간이 소요되는 기존의 교정 분석 방법과 비교하였을 때, 딥 러닝 기반 교정 분석 시스템은 측정의 신뢰성과 유효성 측면에서 임상적으로 허용될 수 있다. 하지만 소아 환자의 교정 분석을 위해 딥 러닝 기반 프로그램을 사용할 때에는 이러한 프로그램의 한계점을 인지하고 올바른 판단으로 사용하는 것이 중요하다.

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.

키워드

참고문헌

  1. 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.
  2. 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. https://doi.org/10.3390/jpm11050356
  3. Baumrind S, Frantz RC : The reliability of head film measurements: 1. Landmark identification. Am J Orthod, 60:111-127, 1971. https://doi.org/10.1016/0002-9416(71)90028-5
  4. Cohen A, Linney A : A Low Cost System for Computer Based Cephalometric Analysis. Br J Orthod, 13:105-108, 1986. https://doi.org/10.1179/bjo.13.2.105
  5. Seki K, Okano T : Exposure reduction in cephalography with a digital photostimulable phosphor imaging system. Dentomaxillofac Radiol, 22:127-130, 1993. https://doi.org/10.1259/dmfr.22.3.8299830
  6. Alqahtani H : Evaluation of an online website-based platform for cephalometric analysis. J Stomatol Oral Maxillofac Surg, 121:53-57, 2020. https://doi.org/10.1016/j.jormas.2019.04.017
  7. 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. https://doi.org/10.1016/j.ajodo.2015.07.030
  8. 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. https://doi.org/10.3390/jcm9020392
  9. 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. https://doi.org/10.1097/SCS.0000000000004901
  10. Arik SO, Ibragimov B, Xing L : Fully automated quantitative cephalometry using convolutional neural networks. J Med Imaging, 4:014501, 2017. https://doi.org/10.1117/1.JMI.4.1.014501
  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. https://doi.org/10.2319/021220-100.1
  12. 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. https://doi.org/10.1016/j.ajodo.2005.12.010
  13. Broadbent BH : A new X-ray technique and its application to orthodontia: the introduction of cephalometric radiography. Angle Orthod, 51:93-114, 1981.
  14. Gravely J, Benzies PM : The clinical significance of tracing error in cephalometry. Br J Orthod, 1:95-101, 1974. https://doi.org/10.1179/bjo.1.3.95
  15. Kim YH : Web based and artificial intelligence driven orthodontic analysis system. J Clin Digit Dent, 1:24-28, 2019.
  16. 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. https://doi.org/10.1259/dmfr/37010910
  17. 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.
  18. Naoumova J, Lindman R : A comparison of manual traced images and corresponding scanned radiographs digitally traced. Eur J Orthod, 31:247-253, 2009. https://doi.org/10.1093/ejo/cjn110
  19. 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. https://doi.org/10.1093/ejo/cjl065
  20. Baumrind S, Frantz RC : The reliability of head film measurements: 2. Conventional angular and linear measures. Am J Orthod, 60:505-517, 1971. https://doi.org/10.1016/0002-9416(71)90116-3
  21. Lim KF, Foong K : Phosphor-stimulated computed cephalometry: reliability of landmark identification. Br J Orthod, 24:301-308, 1997. https://doi.org/10.1093/ortho/24.4.301
  22. Sekiguchi T, Savara BS : Variability of cephalometric landmarks used for face growth studies. Am J Orthod, 61:603-618, 1972. https://doi.org/10.1016/0002-9416(72)90109-1
  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. https://doi.org/10.1007/s10278-017-9997-y