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Automated Bone Age Assessment Using Artificial Intelligence: The Future of Bone Age Assessment

Bone age assessments are a complicated and lengthy process, which are prone to inter- and intra-observer variabilities. Despite the great demand for fully automated systems, developing an accurate and robust bone age assessment solution has remained challenging. The rapidly evolving deep learning te...

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Detalles Bibliográficos
Autores principales: Lee, Byoung-Dai, Lee, Mu Sook
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Society of Radiology 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8076828/
https://www.ncbi.nlm.nih.gov/pubmed/33569930
http://dx.doi.org/10.3348/kjr.2020.0941
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author Lee, Byoung-Dai
Lee, Mu Sook
author_facet Lee, Byoung-Dai
Lee, Mu Sook
author_sort Lee, Byoung-Dai
collection PubMed
description Bone age assessments are a complicated and lengthy process, which are prone to inter- and intra-observer variabilities. Despite the great demand for fully automated systems, developing an accurate and robust bone age assessment solution has remained challenging. The rapidly evolving deep learning technology has shown promising results in automated bone age assessment. In this review article, we will provide information regarding the history of automated bone age assessments, discuss the current status, and present a literature review, as well as the future directions of artificial intelligence-based bone age assessments.
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spelling pubmed-80768282021-05-06 Automated Bone Age Assessment Using Artificial Intelligence: The Future of Bone Age Assessment Lee, Byoung-Dai Lee, Mu Sook Korean J Radiol Pediatric Imaging Bone age assessments are a complicated and lengthy process, which are prone to inter- and intra-observer variabilities. Despite the great demand for fully automated systems, developing an accurate and robust bone age assessment solution has remained challenging. The rapidly evolving deep learning technology has shown promising results in automated bone age assessment. In this review article, we will provide information regarding the history of automated bone age assessments, discuss the current status, and present a literature review, as well as the future directions of artificial intelligence-based bone age assessments. The Korean Society of Radiology 2021-05 2021-01-19 /pmc/articles/PMC8076828/ /pubmed/33569930 http://dx.doi.org/10.3348/kjr.2020.0941 Text en Copyright © 2021 The Korean Society of Radiology https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Pediatric Imaging
Lee, Byoung-Dai
Lee, Mu Sook
Automated Bone Age Assessment Using Artificial Intelligence: The Future of Bone Age Assessment
title Automated Bone Age Assessment Using Artificial Intelligence: The Future of Bone Age Assessment
title_full Automated Bone Age Assessment Using Artificial Intelligence: The Future of Bone Age Assessment
title_fullStr Automated Bone Age Assessment Using Artificial Intelligence: The Future of Bone Age Assessment
title_full_unstemmed Automated Bone Age Assessment Using Artificial Intelligence: The Future of Bone Age Assessment
title_short Automated Bone Age Assessment Using Artificial Intelligence: The Future of Bone Age Assessment
title_sort automated bone age assessment using artificial intelligence: the future of bone age assessment
topic Pediatric Imaging
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8076828/
https://www.ncbi.nlm.nih.gov/pubmed/33569930
http://dx.doi.org/10.3348/kjr.2020.0941
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