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A machine learning model of microscopic agglutination test for diagnosis of leptospirosis
Leptospirosis is a zoonosis caused by the pathogenic bacterium Leptospira. The Microscopic Agglutination Test (MAT) is widely used as the gold standard for diagnosis of leptospirosis. In this method, diluted patient serum is mixed with serotype-determined Leptospires, and the presence or absence of...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8594833/ https://www.ncbi.nlm.nih.gov/pubmed/34784387 http://dx.doi.org/10.1371/journal.pone.0259907 |
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author | Oyamada, Yuji Ozuru, Ryo Masuzawa, Toshiyuki Miyahara, Satoshi Nikaido, Yasuhiko Obata, Fumiko Saito, Mitsumasa Villanueva, Sharon Yvette Angelina M. Fujii, Jun |
author_facet | Oyamada, Yuji Ozuru, Ryo Masuzawa, Toshiyuki Miyahara, Satoshi Nikaido, Yasuhiko Obata, Fumiko Saito, Mitsumasa Villanueva, Sharon Yvette Angelina M. Fujii, Jun |
author_sort | Oyamada, Yuji |
collection | PubMed |
description | Leptospirosis is a zoonosis caused by the pathogenic bacterium Leptospira. The Microscopic Agglutination Test (MAT) is widely used as the gold standard for diagnosis of leptospirosis. In this method, diluted patient serum is mixed with serotype-determined Leptospires, and the presence or absence of aggregation is determined under a dark-field microscope to calculate the antibody titer. Problems of the current MAT method are 1) a requirement of examining many specimens per sample, and 2) a need of distinguishing contaminants from true aggregates to accurately identify positivity. Therefore, increasing efficiency and accuracy are the key to refine MAT. It is possible to achieve efficiency and standardize accuracy at the same time by automating the decision-making process. In this study, we built an automatic identification algorithm of MAT using a machine learning method to determine agglutination within microscopic images. The machine learned the features from 316 positive and 230 negative MAT images created with sera of Leptospira-infected (positive) and non-infected (negative) hamsters, respectively. In addition to the acquired original images, wavelet-transformed images were also considered as features. We utilized a support vector machine (SVM) as a proposed decision method. We validated the trained SVMs with 210 positive and 154 negative images. When the features were obtained from original or wavelet-transformed images, all negative images were misjudged as positive, and the classification performance was very low with sensitivity of 1 and specificity of 0. In contrast, when the histograms of wavelet coefficients were used as features, the performance was greatly improved with sensitivity of 0.99 and specificity of 0.99. We confirmed that the current algorithm judges the positive or negative of agglutinations in MAT images and gives the further possibility of automatizing MAT procedure. |
format | Online Article Text |
id | pubmed-8594833 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-85948332021-11-17 A machine learning model of microscopic agglutination test for diagnosis of leptospirosis Oyamada, Yuji Ozuru, Ryo Masuzawa, Toshiyuki Miyahara, Satoshi Nikaido, Yasuhiko Obata, Fumiko Saito, Mitsumasa Villanueva, Sharon Yvette Angelina M. Fujii, Jun PLoS One Research Article Leptospirosis is a zoonosis caused by the pathogenic bacterium Leptospira. The Microscopic Agglutination Test (MAT) is widely used as the gold standard for diagnosis of leptospirosis. In this method, diluted patient serum is mixed with serotype-determined Leptospires, and the presence or absence of aggregation is determined under a dark-field microscope to calculate the antibody titer. Problems of the current MAT method are 1) a requirement of examining many specimens per sample, and 2) a need of distinguishing contaminants from true aggregates to accurately identify positivity. Therefore, increasing efficiency and accuracy are the key to refine MAT. It is possible to achieve efficiency and standardize accuracy at the same time by automating the decision-making process. In this study, we built an automatic identification algorithm of MAT using a machine learning method to determine agglutination within microscopic images. The machine learned the features from 316 positive and 230 negative MAT images created with sera of Leptospira-infected (positive) and non-infected (negative) hamsters, respectively. In addition to the acquired original images, wavelet-transformed images were also considered as features. We utilized a support vector machine (SVM) as a proposed decision method. We validated the trained SVMs with 210 positive and 154 negative images. When the features were obtained from original or wavelet-transformed images, all negative images were misjudged as positive, and the classification performance was very low with sensitivity of 1 and specificity of 0. In contrast, when the histograms of wavelet coefficients were used as features, the performance was greatly improved with sensitivity of 0.99 and specificity of 0.99. We confirmed that the current algorithm judges the positive or negative of agglutinations in MAT images and gives the further possibility of automatizing MAT procedure. Public Library of Science 2021-11-16 /pmc/articles/PMC8594833/ /pubmed/34784387 http://dx.doi.org/10.1371/journal.pone.0259907 Text en © 2021 Oyamada et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Oyamada, Yuji Ozuru, Ryo Masuzawa, Toshiyuki Miyahara, Satoshi Nikaido, Yasuhiko Obata, Fumiko Saito, Mitsumasa Villanueva, Sharon Yvette Angelina M. Fujii, Jun A machine learning model of microscopic agglutination test for diagnosis of leptospirosis |
title | A machine learning model of microscopic agglutination test for diagnosis of leptospirosis |
title_full | A machine learning model of microscopic agglutination test for diagnosis of leptospirosis |
title_fullStr | A machine learning model of microscopic agglutination test for diagnosis of leptospirosis |
title_full_unstemmed | A machine learning model of microscopic agglutination test for diagnosis of leptospirosis |
title_short | A machine learning model of microscopic agglutination test for diagnosis of leptospirosis |
title_sort | machine learning model of microscopic agglutination test for diagnosis of leptospirosis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8594833/ https://www.ncbi.nlm.nih.gov/pubmed/34784387 http://dx.doi.org/10.1371/journal.pone.0259907 |
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