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Toward co-design of an AI solution for detection of diarrheal pathogens in drinking water within resource-constrained contexts
Despite successes on the Sustainable Development Goals for access to improved water sources and sanitation, many low and middle-income countries (LMICs) continue to struggle with high rates of diarrheal disease. In Guatemala, 98% of water sources are estimated to have E. coli contamination. This pro...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10021207/ https://www.ncbi.nlm.nih.gov/pubmed/36962801 http://dx.doi.org/10.1371/journal.pgph.0000918 |
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author | Hall-Clifford, Rachel Arzu, Alejandro Contreras, Saul Croissert Muguercia, Maria Gabriela de Leon Figueroa, Diana Ximena Ochoa Elias, Maria Valeria Soto Fernández, Anna Yunuen Tariq, Amara Banerjee, Imon Pennington, Pamela |
author_facet | Hall-Clifford, Rachel Arzu, Alejandro Contreras, Saul Croissert Muguercia, Maria Gabriela de Leon Figueroa, Diana Ximena Ochoa Elias, Maria Valeria Soto Fernández, Anna Yunuen Tariq, Amara Banerjee, Imon Pennington, Pamela |
author_sort | Hall-Clifford, Rachel |
collection | PubMed |
description | Despite successes on the Sustainable Development Goals for access to improved water sources and sanitation, many low and middle-income countries (LMICs) continue to struggle with high rates of diarrheal disease. In Guatemala, 98% of water sources are estimated to have E. coli contamination. This project moves toward a novel low-cost approach to bridge the gap between the microbiologic identification of E. coli and the vast impact that this pathogen has on human health within marginalized communities using co-designed community-based tools, low-cost technology, and AI. An agile co-design process was followed with water quality stakeholders, community staff, and local graphic design artists to develop a community water quality education mobile app. A series of alpha- and beta-testers completed interactive demonstration, feedback, and in-depth interview sessions. A microbiology lab in Guatemala developed and piloted field protocols with lay community workers to collect and process water samples. A preliminary artificial intelligence (AI) algorithm was developed to detect the presence of E. coli in images generated from community-derived water samples. The mobile app emerged as a pictorial and audio-driven community-facing tool. The field protocol for water sampling and testing was successfully implemented by lay community workers. Feedback from the community workers indicated both desire and ability to conduct the water sampling and testing protocol under field conditions. However, images derived from the low-cost $2 microscope in field conditions were not of a suitable quality for AI object detection of E. coli, and additional low-cost technologies are being considered. The preliminary AI object detection algorithm from lab-derived images performed at 94% accuracy in identifying E. coli in comparison to the Chromocult gold-standard. |
format | Online Article Text |
id | pubmed-10021207 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-100212072023-03-17 Toward co-design of an AI solution for detection of diarrheal pathogens in drinking water within resource-constrained contexts Hall-Clifford, Rachel Arzu, Alejandro Contreras, Saul Croissert Muguercia, Maria Gabriela de Leon Figueroa, Diana Ximena Ochoa Elias, Maria Valeria Soto Fernández, Anna Yunuen Tariq, Amara Banerjee, Imon Pennington, Pamela PLOS Glob Public Health Research Article Despite successes on the Sustainable Development Goals for access to improved water sources and sanitation, many low and middle-income countries (LMICs) continue to struggle with high rates of diarrheal disease. In Guatemala, 98% of water sources are estimated to have E. coli contamination. This project moves toward a novel low-cost approach to bridge the gap between the microbiologic identification of E. coli and the vast impact that this pathogen has on human health within marginalized communities using co-designed community-based tools, low-cost technology, and AI. An agile co-design process was followed with water quality stakeholders, community staff, and local graphic design artists to develop a community water quality education mobile app. A series of alpha- and beta-testers completed interactive demonstration, feedback, and in-depth interview sessions. A microbiology lab in Guatemala developed and piloted field protocols with lay community workers to collect and process water samples. A preliminary artificial intelligence (AI) algorithm was developed to detect the presence of E. coli in images generated from community-derived water samples. The mobile app emerged as a pictorial and audio-driven community-facing tool. The field protocol for water sampling and testing was successfully implemented by lay community workers. Feedback from the community workers indicated both desire and ability to conduct the water sampling and testing protocol under field conditions. However, images derived from the low-cost $2 microscope in field conditions were not of a suitable quality for AI object detection of E. coli, and additional low-cost technologies are being considered. The preliminary AI object detection algorithm from lab-derived images performed at 94% accuracy in identifying E. coli in comparison to the Chromocult gold-standard. Public Library of Science 2022-08-16 /pmc/articles/PMC10021207/ /pubmed/36962801 http://dx.doi.org/10.1371/journal.pgph.0000918 Text en © 2022 Hall-Clifford 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 Hall-Clifford, Rachel Arzu, Alejandro Contreras, Saul Croissert Muguercia, Maria Gabriela de Leon Figueroa, Diana Ximena Ochoa Elias, Maria Valeria Soto Fernández, Anna Yunuen Tariq, Amara Banerjee, Imon Pennington, Pamela Toward co-design of an AI solution for detection of diarrheal pathogens in drinking water within resource-constrained contexts |
title | Toward co-design of an AI solution for detection of diarrheal pathogens in drinking water within resource-constrained contexts |
title_full | Toward co-design of an AI solution for detection of diarrheal pathogens in drinking water within resource-constrained contexts |
title_fullStr | Toward co-design of an AI solution for detection of diarrheal pathogens in drinking water within resource-constrained contexts |
title_full_unstemmed | Toward co-design of an AI solution for detection of diarrheal pathogens in drinking water within resource-constrained contexts |
title_short | Toward co-design of an AI solution for detection of diarrheal pathogens in drinking water within resource-constrained contexts |
title_sort | toward co-design of an ai solution for detection of diarrheal pathogens in drinking water within resource-constrained contexts |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10021207/ https://www.ncbi.nlm.nih.gov/pubmed/36962801 http://dx.doi.org/10.1371/journal.pgph.0000918 |
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