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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Korean Society of Radiology
2021
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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. |
format | Online Article Text |
id | pubmed-8076828 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Korean Society of Radiology |
record_format | MEDLINE/PubMed |
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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