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Dicentric chromosome assay using a deep learning-based automated system
The dicentric chromosome assay is the “gold standard” in biodosimetry for estimating radiation exposure. However, its large-scale deployment is limited owing to its time-consuming nature and requirement for expert reviewers. Therefore, a recently developed automated system was evaluated for the dice...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9772420/ https://www.ncbi.nlm.nih.gov/pubmed/36543843 http://dx.doi.org/10.1038/s41598-022-25856-1 |
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author | Jeong, Soo Kyung Oh, Su Jung Kim, Song-Hyun Jang, Seungsoo Kang, Yeong-Rok Kim, HyoJin Kye, Yong Uk Lee, Seong Hun Lee, Chang Geun Park, Moon-Taek Kim, Joong Sun Jeong, Min Ho Jo, Wol Soon |
author_facet | Jeong, Soo Kyung Oh, Su Jung Kim, Song-Hyun Jang, Seungsoo Kang, Yeong-Rok Kim, HyoJin Kye, Yong Uk Lee, Seong Hun Lee, Chang Geun Park, Moon-Taek Kim, Joong Sun Jeong, Min Ho Jo, Wol Soon |
author_sort | Jeong, Soo Kyung |
collection | PubMed |
description | The dicentric chromosome assay is the “gold standard” in biodosimetry for estimating radiation exposure. However, its large-scale deployment is limited owing to its time-consuming nature and requirement for expert reviewers. Therefore, a recently developed automated system was evaluated for the dicentric chromosome assay. A previously constructed deep learning-based automatic dose-estimation system (DLADES) was used to construct dose curves and calculate estimated doses. Blood samples from two donors were exposed to cobalt-60 gamma rays (0–4 Gy, 0.8 Gy/min). The DLADES efficiently identified monocentric and dicentric chromosomes but showed impaired recognition of complete cells with 46 chromosomes. We estimated the chromosome number of each “Accepted” sample in the DLADES and sorted similar-quality images by removing outliers using the 1.5IQR method. Eleven of the 12 data points followed Poisson distribution. Blind samples were prepared for each dose to verify the accuracy of the estimated dose generated by the curve. The estimated dose was calculated using Merkle’s method. The actual dose for each sample was within the 95% confidence limits of the estimated dose. Sorting similar-quality images using chromosome numbers is crucial for the automated dicentric chromosome assay. We successfully constructed a dose–response curve and determined the estimated dose using the DLADES. |
format | Online Article Text |
id | pubmed-9772420 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-97724202022-12-23 Dicentric chromosome assay using a deep learning-based automated system Jeong, Soo Kyung Oh, Su Jung Kim, Song-Hyun Jang, Seungsoo Kang, Yeong-Rok Kim, HyoJin Kye, Yong Uk Lee, Seong Hun Lee, Chang Geun Park, Moon-Taek Kim, Joong Sun Jeong, Min Ho Jo, Wol Soon Sci Rep Article The dicentric chromosome assay is the “gold standard” in biodosimetry for estimating radiation exposure. However, its large-scale deployment is limited owing to its time-consuming nature and requirement for expert reviewers. Therefore, a recently developed automated system was evaluated for the dicentric chromosome assay. A previously constructed deep learning-based automatic dose-estimation system (DLADES) was used to construct dose curves and calculate estimated doses. Blood samples from two donors were exposed to cobalt-60 gamma rays (0–4 Gy, 0.8 Gy/min). The DLADES efficiently identified monocentric and dicentric chromosomes but showed impaired recognition of complete cells with 46 chromosomes. We estimated the chromosome number of each “Accepted” sample in the DLADES and sorted similar-quality images by removing outliers using the 1.5IQR method. Eleven of the 12 data points followed Poisson distribution. Blind samples were prepared for each dose to verify the accuracy of the estimated dose generated by the curve. The estimated dose was calculated using Merkle’s method. The actual dose for each sample was within the 95% confidence limits of the estimated dose. Sorting similar-quality images using chromosome numbers is crucial for the automated dicentric chromosome assay. We successfully constructed a dose–response curve and determined the estimated dose using the DLADES. Nature Publishing Group UK 2022-12-21 /pmc/articles/PMC9772420/ /pubmed/36543843 http://dx.doi.org/10.1038/s41598-022-25856-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Jeong, Soo Kyung Oh, Su Jung Kim, Song-Hyun Jang, Seungsoo Kang, Yeong-Rok Kim, HyoJin Kye, Yong Uk Lee, Seong Hun Lee, Chang Geun Park, Moon-Taek Kim, Joong Sun Jeong, Min Ho Jo, Wol Soon Dicentric chromosome assay using a deep learning-based automated system |
title | Dicentric chromosome assay using a deep learning-based automated system |
title_full | Dicentric chromosome assay using a deep learning-based automated system |
title_fullStr | Dicentric chromosome assay using a deep learning-based automated system |
title_full_unstemmed | Dicentric chromosome assay using a deep learning-based automated system |
title_short | Dicentric chromosome assay using a deep learning-based automated system |
title_sort | dicentric chromosome assay using a deep learning-based automated system |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9772420/ https://www.ncbi.nlm.nih.gov/pubmed/36543843 http://dx.doi.org/10.1038/s41598-022-25856-1 |
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