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Prediction of hand-wrist maturation stages based on cervical vertebrae images using artificial intelligence

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Abstract
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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.
Author(s)
김동욱김진희김태성김태우김윤지송인석안병덕이동렬주재걸
Issued Date
2021
Type
Article
Keyword
과제신청관계로 사전 승인함. Age Determination by SkeletonAnalysisArtificial IntelligenceBone DevelopmentCephalometryCervical Vertebrae - diagnostic imagingcervical vertebrae maturationChildensemble machine learninghand‐wrist bone ageHumansMachine learningskeletal maturationUsageWrist - diagnostic imaging
DOI
10.1111/ocr.12514
URI
https://oak.ulsan.ac.kr/handle/2021.oak/8735
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_proquest_miscellaneous_2562516435&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Prediction%20of%20hand-wrist%20maturation%20stages%20based%20on%20cervical%20vertebrae%20images%20using%20artificial%20intelligence&offset=0&pcAvailability=true
Publisher
Orthodontics & Craniofacial Research
Location
영국
Language
한국어
ISSN
1601-6335
Citation Volume
24
Citation Number
S2
Citation Start Page
67
Citation End Page
75
Appears in Collections:
Medicine > Medicine
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