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Automatic classification of IgA endomysial antibody test for celiac disease: a new method deploying machine learning
Widespread use of endomysial autoantibody (EmA) test in diagnostics of celiac disease is limited due to its subjectivity and its requirement of an expert evaluator. The study aimed to determine whether machine learning can be applied to create a new observer-independent method of automatic assessmen...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6592927/ https://www.ncbi.nlm.nih.gov/pubmed/31239486 http://dx.doi.org/10.1038/s41598-019-45679-x |
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author | Caetano dos Santos, Florentino Luciano Michalek, Irmina Maria Laurila, Kaija Kaukinen, Katri Hyttinen, Jari Lindfors, Katri |
author_facet | Caetano dos Santos, Florentino Luciano Michalek, Irmina Maria Laurila, Kaija Kaukinen, Katri Hyttinen, Jari Lindfors, Katri |
author_sort | Caetano dos Santos, Florentino Luciano |
collection | PubMed |
description | Widespread use of endomysial autoantibody (EmA) test in diagnostics of celiac disease is limited due to its subjectivity and its requirement of an expert evaluator. The study aimed to determine whether machine learning can be applied to create a new observer-independent method of automatic assessment and classification of the EmA test for celiac disease. The study material comprised of 2597 high-quality IgA-class EmA images collected in 2017–2018. According to standard procedure, highly-experienced professional classified samples into the following four classes: I - positive, II - negative, III - IgA deficient, and IV - equivocal. Machine learning was deployed to create a classification model. The sensitivity and specificity of the model were 82.84% and 99.40%, respectively. The accuracy was 96.80%. The classification error was 3.20%. The area under the curve was 99.67%, 99.61%, 100%, and 99.89%, for I, II, III, and IV class, respectively. The mean assessment time per image was 16.11 seconds. This is the first study deploying machine learning for the automatic classification of IgA-class EmA test for celiac disease. The results indicate that using machine learning enables quick and precise EmA test analysis that can be further developed to simplify EmA analysis. |
format | Online Article Text |
id | pubmed-6592927 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-65929272019-07-03 Automatic classification of IgA endomysial antibody test for celiac disease: a new method deploying machine learning Caetano dos Santos, Florentino Luciano Michalek, Irmina Maria Laurila, Kaija Kaukinen, Katri Hyttinen, Jari Lindfors, Katri Sci Rep Article Widespread use of endomysial autoantibody (EmA) test in diagnostics of celiac disease is limited due to its subjectivity and its requirement of an expert evaluator. The study aimed to determine whether machine learning can be applied to create a new observer-independent method of automatic assessment and classification of the EmA test for celiac disease. The study material comprised of 2597 high-quality IgA-class EmA images collected in 2017–2018. According to standard procedure, highly-experienced professional classified samples into the following four classes: I - positive, II - negative, III - IgA deficient, and IV - equivocal. Machine learning was deployed to create a classification model. The sensitivity and specificity of the model were 82.84% and 99.40%, respectively. The accuracy was 96.80%. The classification error was 3.20%. The area under the curve was 99.67%, 99.61%, 100%, and 99.89%, for I, II, III, and IV class, respectively. The mean assessment time per image was 16.11 seconds. This is the first study deploying machine learning for the automatic classification of IgA-class EmA test for celiac disease. The results indicate that using machine learning enables quick and precise EmA test analysis that can be further developed to simplify EmA analysis. Nature Publishing Group UK 2019-06-25 /pmc/articles/PMC6592927/ /pubmed/31239486 http://dx.doi.org/10.1038/s41598-019-45679-x Text en © The Author(s) 2019 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Caetano dos Santos, Florentino Luciano Michalek, Irmina Maria Laurila, Kaija Kaukinen, Katri Hyttinen, Jari Lindfors, Katri Automatic classification of IgA endomysial antibody test for celiac disease: a new method deploying machine learning |
title | Automatic classification of IgA endomysial antibody test for celiac disease: a new method deploying machine learning |
title_full | Automatic classification of IgA endomysial antibody test for celiac disease: a new method deploying machine learning |
title_fullStr | Automatic classification of IgA endomysial antibody test for celiac disease: a new method deploying machine learning |
title_full_unstemmed | Automatic classification of IgA endomysial antibody test for celiac disease: a new method deploying machine learning |
title_short | Automatic classification of IgA endomysial antibody test for celiac disease: a new method deploying machine learning |
title_sort | automatic classification of iga endomysial antibody test for celiac disease: a new method deploying machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6592927/ https://www.ncbi.nlm.nih.gov/pubmed/31239486 http://dx.doi.org/10.1038/s41598-019-45679-x |
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