Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
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
_version_ 1784908427777015808
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
work_keys_str_mv AT hallcliffordrachel towardcodesignofanaisolutionfordetectionofdiarrhealpathogensindrinkingwaterwithinresourceconstrainedcontexts
AT arzualejandro towardcodesignofanaisolutionfordetectionofdiarrhealpathogensindrinkingwaterwithinresourceconstrainedcontexts
AT contrerassaul towardcodesignofanaisolutionfordetectionofdiarrhealpathogensindrinkingwaterwithinresourceconstrainedcontexts
AT croissertmuguerciamariagabriela towardcodesignofanaisolutionfordetectionofdiarrhealpathogensindrinkingwaterwithinresourceconstrainedcontexts
AT deleonfigueroadianaximena towardcodesignofanaisolutionfordetectionofdiarrhealpathogensindrinkingwaterwithinresourceconstrainedcontexts
AT ochoaeliasmariavaleria towardcodesignofanaisolutionfordetectionofdiarrhealpathogensindrinkingwaterwithinresourceconstrainedcontexts
AT sotofernandezannayunuen towardcodesignofanaisolutionfordetectionofdiarrhealpathogensindrinkingwaterwithinresourceconstrainedcontexts
AT tariqamara towardcodesignofanaisolutionfordetectionofdiarrhealpathogensindrinkingwaterwithinresourceconstrainedcontexts
AT banerjeeimon towardcodesignofanaisolutionfordetectionofdiarrhealpathogensindrinkingwaterwithinresourceconstrainedcontexts
AT penningtonpamela towardcodesignofanaisolutionfordetectionofdiarrhealpathogensindrinkingwaterwithinresourceconstrainedcontexts