Mobile microscopy and telemedicine platform assisted by deep learning for the quantification of Trichuris trichiura infection.
Soil-transmitted helminths (STH) are the most prevalent pathogens among the group of neglected tropical diseases (NTDs). The Kato-Katz technique is the diagnosis method recommended by the World Health Organization (WHO) although it often presents a decreased sensitivity in low transmission settings...
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ftdoajarticles:oai:doaj.org/article:b68efe3111fc440fac703e9e3903abb9 2023-05-15T15:15:32+02:00 Mobile microscopy and telemedicine platform assisted by deep learning for the quantification of Trichuris trichiura infection. Elena Dacal David Bermejo-Peláez Lin Lin Elisa Álamo Daniel Cuadrado Álvaro Martínez Adriana Mousa María Postigo Alicia Soto Endre Sukosd Alexander Vladimirov Charles Mwandawiro Paul Gichuki Nana Aba Williams José Muñoz Stella Kepha Miguel Luengo-Oroz 2021-09-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0009677 https://doaj.org/article/b68efe3111fc440fac703e9e3903abb9 EN eng Public Library of Science (PLoS) https://doi.org/10.1371/journal.pntd.0009677 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0009677 https://doaj.org/article/b68efe3111fc440fac703e9e3903abb9 PLoS Neglected Tropical Diseases, Vol 15, Iss 9, p e0009677 (2021) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2021 ftdoajarticles https://doi.org/10.1371/journal.pntd.0009677 2022-12-31T15:09:50Z Soil-transmitted helminths (STH) are the most prevalent pathogens among the group of neglected tropical diseases (NTDs). The Kato-Katz technique is the diagnosis method recommended by the World Health Organization (WHO) although it often presents a decreased sensitivity in low transmission settings and it is labour intensive. Visual reading of Kato-Katz preparations requires the samples to be analyzed in a short period of time since its preparation. Digitizing the samples could provide a solution which allows to store the samples in a digital database and perform remote analysis. Artificial intelligence (AI) methods based on digitized samples can support diagnosis by performing an objective and automatic quantification of disease infection. In this work, we propose an end-to-end pipeline for microscopy image digitization and automatic analysis of digitized images of STH. Our solution includes (a) a digitization system based on a mobile app that digitizes microscope samples using a 3D printed microscope adapter, (b) a telemedicine platform for remote analysis and labelling, and (c) novel deep learning algorithms for automatic assessment and quantification of parasitological infections by STH. The deep learning algorithm has been trained and tested on 51 slides of stool samples containing 949 Trichuris spp. eggs from 6 different subjects. The algorithm evaluation was performed using a cross-validation strategy, obtaining a mean precision of 98.44% and a mean recall of 80.94%. The results also proved the potential of generalization capability of the method at identifying different types of helminth eggs. Additionally, the AI-assisted quantification of STH based on digitized samples has been compared to the one performed using conventional microscopy, showing a good agreement between measurements. In conclusion, this work has presented a comprehensive pipeline using smartphone-assisted microscopy. It is integrated with a telemedicine platform for automatic image analysis and quantification of STH infection using AI ... Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLOS Neglected Tropical Diseases 15 9 e0009677 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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English |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Elena Dacal David Bermejo-Peláez Lin Lin Elisa Álamo Daniel Cuadrado Álvaro Martínez Adriana Mousa María Postigo Alicia Soto Endre Sukosd Alexander Vladimirov Charles Mwandawiro Paul Gichuki Nana Aba Williams José Muñoz Stella Kepha Miguel Luengo-Oroz Mobile microscopy and telemedicine platform assisted by deep learning for the quantification of Trichuris trichiura infection. |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
Soil-transmitted helminths (STH) are the most prevalent pathogens among the group of neglected tropical diseases (NTDs). The Kato-Katz technique is the diagnosis method recommended by the World Health Organization (WHO) although it often presents a decreased sensitivity in low transmission settings and it is labour intensive. Visual reading of Kato-Katz preparations requires the samples to be analyzed in a short period of time since its preparation. Digitizing the samples could provide a solution which allows to store the samples in a digital database and perform remote analysis. Artificial intelligence (AI) methods based on digitized samples can support diagnosis by performing an objective and automatic quantification of disease infection. In this work, we propose an end-to-end pipeline for microscopy image digitization and automatic analysis of digitized images of STH. Our solution includes (a) a digitization system based on a mobile app that digitizes microscope samples using a 3D printed microscope adapter, (b) a telemedicine platform for remote analysis and labelling, and (c) novel deep learning algorithms for automatic assessment and quantification of parasitological infections by STH. The deep learning algorithm has been trained and tested on 51 slides of stool samples containing 949 Trichuris spp. eggs from 6 different subjects. The algorithm evaluation was performed using a cross-validation strategy, obtaining a mean precision of 98.44% and a mean recall of 80.94%. The results also proved the potential of generalization capability of the method at identifying different types of helminth eggs. Additionally, the AI-assisted quantification of STH based on digitized samples has been compared to the one performed using conventional microscopy, showing a good agreement between measurements. In conclusion, this work has presented a comprehensive pipeline using smartphone-assisted microscopy. It is integrated with a telemedicine platform for automatic image analysis and quantification of STH infection using AI ... |
format |
Article in Journal/Newspaper |
author |
Elena Dacal David Bermejo-Peláez Lin Lin Elisa Álamo Daniel Cuadrado Álvaro Martínez Adriana Mousa María Postigo Alicia Soto Endre Sukosd Alexander Vladimirov Charles Mwandawiro Paul Gichuki Nana Aba Williams José Muñoz Stella Kepha Miguel Luengo-Oroz |
author_facet |
Elena Dacal David Bermejo-Peláez Lin Lin Elisa Álamo Daniel Cuadrado Álvaro Martínez Adriana Mousa María Postigo Alicia Soto Endre Sukosd Alexander Vladimirov Charles Mwandawiro Paul Gichuki Nana Aba Williams José Muñoz Stella Kepha Miguel Luengo-Oroz |
author_sort |
Elena Dacal |
title |
Mobile microscopy and telemedicine platform assisted by deep learning for the quantification of Trichuris trichiura infection. |
title_short |
Mobile microscopy and telemedicine platform assisted by deep learning for the quantification of Trichuris trichiura infection. |
title_full |
Mobile microscopy and telemedicine platform assisted by deep learning for the quantification of Trichuris trichiura infection. |
title_fullStr |
Mobile microscopy and telemedicine platform assisted by deep learning for the quantification of Trichuris trichiura infection. |
title_full_unstemmed |
Mobile microscopy and telemedicine platform assisted by deep learning for the quantification of Trichuris trichiura infection. |
title_sort |
mobile microscopy and telemedicine platform assisted by deep learning for the quantification of trichuris trichiura infection. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2021 |
url |
https://doi.org/10.1371/journal.pntd.0009677 https://doaj.org/article/b68efe3111fc440fac703e9e3903abb9 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 15, Iss 9, p e0009677 (2021) |
op_relation |
https://doi.org/10.1371/journal.pntd.0009677 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0009677 https://doaj.org/article/b68efe3111fc440fac703e9e3903abb9 |
op_doi |
https://doi.org/10.1371/journal.pntd.0009677 |
container_title |
PLOS Neglected Tropical Diseases |
container_volume |
15 |
container_issue |
9 |
container_start_page |
e0009677 |
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1766345899479400448 |