Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Caetano dos Santos, Florentino Luciano, Michalek, Irmina Maria, Laurila, Kaija, Kaukinen, Katri, Hyttinen, Jari, Lindfors, Katri
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
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
_version_ 1783429946978336768
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
work_keys_str_mv AT caetanodossantosflorentinoluciano automaticclassificationofigaendomysialantibodytestforceliacdiseaseanewmethoddeployingmachinelearning
AT michalekirminamaria automaticclassificationofigaendomysialantibodytestforceliacdiseaseanewmethoddeployingmachinelearning
AT laurilakaija automaticclassificationofigaendomysialantibodytestforceliacdiseaseanewmethoddeployingmachinelearning
AT kaukinenkatri automaticclassificationofigaendomysialantibodytestforceliacdiseaseanewmethoddeployingmachinelearning
AT hyttinenjari automaticclassificationofigaendomysialantibodytestforceliacdiseaseanewmethoddeployingmachinelearning
AT lindforskatri automaticclassificationofigaendomysialantibodytestforceliacdiseaseanewmethoddeployingmachinelearning