Comparative Validation of the Mixed and Permanent Dentition at Web-Based Artificial Intelligence Cephalometric Analysis
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초록
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.
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.
참고문헌 (23)
- 김지연, "유치열기 정상교합아동의 측모두부방사선 계측연구", 대한소아치과학회지, 32(4), pp.649~656 (2005), 대한소아치과학회
- Kim YH, "Influence of the Depth of the Convolutional Neural Networks on an Artificial Intelligence Model for Diagnosis of Orthognathic Surgery", J Pers Med, 11, pp.356~366 (2021)
- Baumrind S, "The reliability of head film measurements: 1. Landmark identification", Am J Orthod, 60, pp.111~127 (1971)
- Cohen A, "A Low Cost System for Computer Based Cephalometric Analysis", Br J Orthod, 13, pp.105~108 (1986)
- Seki K, "Exposure reduction in cephalography with a digital photostimulable phosphor imaging system", Dentomaxillofac Radiol, 22, pp.127~130 (1993)
- Alqahtani H, "Evaluation of an online website-based platform for cephalometric analysis", J Stomatol Oral Maxillofac Surg, 121, pp.53~57 (2020)
- Jung SK, "New approach for the diagnosis of extractions with neural network machine learning", Am J Orthod Dentofacial Orthop, 149, pp.127~133 (2016)
- Lee KS, "Evaluation of transfer learning with deep convolutional neural networks for screening osteoporosis in dental panoramic radiographs", J Clin Med, 9, pp.392~404 (2020)
- Nishimoto S, "Personal computer-based cephalometric landmark detection with deep learning, using cephalograms on the internet", J Craniofac Surg, 30, pp.91~95 (2019)
- Arik SÖ, "Fully automated quantitative cephalometry using convolutional neural networks", J Med Imaging, 4, pp.014501~ (2017)
- Hwang HW, "Evaluation of automated cephalometric analysis based on the latest deep learning method", Angle Orthod, 91, pp.329~335 (2021)
- Santoro M, "Accuracy of digital and analogue cephalometric measurements assessed with the sandwich technique", Am J Orthod Dentofacial Orthop, 129, pp.345~351 (2006)
- Broadbent BH, "A new X-ray technique and its application to orthodontia : the introduction of cephalometric radiography", Angle Orthod, 51, pp.93~114 (1981)
- Gravely J, "The clinical significance of tracing error in cephalometry", Br J Orthod, 1, pp.95~101 (1974)
- Kim YH, "Web based and artificial intelligence driven orthodontic analysis system", J Clin Digit Dent, 1, pp.24~28 (2019)
- Albarakati S, "The reliability and reproducibility of cephalometric measurements : a comparison of conventional and digital methods", Dentomaxillofac Radiol, 41, pp.11~17 (2012)
- Chen YJ, "Comparison of landmark identification in traditional versus computer-aided digital cephalometry", Angle Orthod, 70, pp.387~392 (2000)
- Naoumova J, "A comparison of manual traced images and corresponding scanned radiographs digitally traced", Eur J Orthod, 31, pp.247~253 (2009)
- Sayinsu K, "An evaluation of the errors in cephalometric measurements on scanned cephalometric images and conventional tracings", Eur J Orthod, 29, pp.105~108 (2007)
- Baumrind S, "The reliability of head film measurements: 2. Conventional angular and linear measures", Am J Orthod, 60, pp.505~517 (1971)
- Lim KF, "Phosphor-stimulated computed cephalometry : reliability of landmark identification", Br J Orthod, 24, pp.301~308 (1997)
- Sekiguchi T, "Variability of cephalometric landmarks used for face growth studies", Am J Orthod, 61, pp.603~618 (1972)
- Chi J, "Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network", J Digit Imaging, 30, pp.477~486 (2017)