Kim, Dong-Wook; Kim, Jinhee; Kim, Taesung; Kim, Taewoo; Kim, Yoon-Ji; Song, In-Seok; Ahn, Byungduk; Choo, Jaegul; Lee, Dong-Yul
Orthodontics & craniofacial research
2021Aug ; 11 ( 1 ) :.
PMID : 34405944
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Kim, Dong-Wook - Department of Orthodontics, Korea University Anam Hospital, Seoul, Korea.
Kim, Jinhee - Graduate School of Artificial Intelligence, Korea Advanced Institute of Science and
Kim, Taesung - Graduate School of Artificial Intelligence, Korea Advanced Institute of Science and
Kim, Taewoo - Graduate School of Artificial Intelligence, Korea Advanced Institute of Science and
Kim, Yoon-Ji - Department of Orthodontics, Asan Medical Center, University of Ulsan College of
Song, In-Seok - Department of Oral and Maxillofacial Surgery, Korea University Anam Hospital, Seoul,
Ahn, Byungduk - Private practice, Seoul, Korea.
Choo, Jaegul - Graduate School of Artificial Intelligence, Korea Advanced Institute of Science and
Lee, Dong-Yul - Department of Orthodontics, Korea University Guro Hospital, Seoul, Korea.
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. CI - ??2021 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
keyword
artificial intelligence; cervical vertebrae maturation; ensemble machine learning; hand-wrist bone age; skeletal maturation
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