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Cited 4 time in webofscience Cited 4 time in scopus
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Prediction of hand-wrist maturation stages based on cervical vertebrae images using artificial intelligence

Authors
Kim, Dong-WookKim, JinheeKim, TaesungKim, TaewooKim, Yoon-JiSong, In-SeokAhn, ByungdukChoo, JaegulLee, Dong-Yul
Issue Date
Dec-2021
Publisher
Blackwell Publishing Inc.
Keywords
artificial intelligence; cervical vertebrae maturation; ensemble machine learning; hand-wrist bone age; skeletal maturation
Citation
Orthodontics and Craniofacial Research, v.24, no.S2, pp.68 - 75
Indexed
SCIE
SCOPUS
Journal Title
Orthodontics and Craniofacial Research
Volume
24
Number
S2
Start Page
68
End Page
75
URI
https://scholarworks.korea.ac.kr/kumedicine/handle/2020.sw.kumedicine/54210
DOI
10.1111/ocr.12514
ISSN
1601-6335
Abstract
Objective To predict the hand-wrist maturation stages based on the cervical vertebrae (CV) images, and to analyse the accuracy of the proposed algorithms. Settings and population A total of 499 pairs of hand-wrist radiographs and lateral cephalograms of 455 orthodontic patients aged 6-18 years were used for developing the prediction model for hand-wrist skeletal maturation stages. Materials and Methods The hand-wrist radiographs and the lateral cephalograms were collected from two university hospitals and a paediatric dental clinic. After identifying the 13 anatomic landmarks of the CV, the width-height ratio, width-perpendicular height ratio and concavity ratio of the CV were used as the morphometric features of the CV. Patients’ chronological age and sex were also included as input data. The ground truth data were the Fishman SMI based on the hand-wrist radiographs. Three specialists determined the ground truth SMI. An ensemble machine learning methods were used to predict the Fishman SMI. Five-fold cross-validation was performed. The mean absolute error (MAE), round MAE and root mean square error (RMSE) values were used to assess the performance of the final ensemble model. Results The final ensemble model consisted of eight machine learning models. The MAE, round MAE and RMSE were 0.90, 0.87 and 1.20, respectively. Conclusion Prediction of hand-wrist SMI based on CV images is possible using machine learning methods. Chronological age and sex increased the prediction accuracy. An automated diagnosis of the skeletal maturation may aid as a decision-supporting tool for evaluating the optimal treatment timing for growing patients.
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2. Clinical Science > Department of Orthodontics > 1. Journal Articles
2. Clinical Science > Department of Oral and Maxillofacial Surgery > 1. Journal Articles

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Song, In Seok
안암병원 (Department of Oral and Maxillofacial Surgery, Anam Hospital)
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