Toward co-design of an AI solution for detection of diarrheal pathogens in drinking water within resource-constrained contexts.

• Type of Material: Article
• Authors: Rachel Hall-Clifford, Emory University; Alejandro Arzu, Emory University; Saul Contreras, Universidad del Valle de Guatemala; Maria Gabriela Croissert Muguercia, Universidad del Valle de Guatemala; Diana Ximena de Leon Figueroa, Universidad del Valle de Guatemala; Imon Banerjee, Emory University; Maria Valeria Ochoa Elias, Universidad del Valle de Guatemala; Anna Yunuen Soto Fernández, Universidad del Valle de Guatemala; Amara Tariq, Mayo Clinic, Phoenix; Pamela Pennington, Universidad del Valle de Guatemala
• Language: English
• Date: 2022
• Publisher: PLOS
• Publication Version: Final Published Version
• Copyright Statement: © 2022 Hall-Clifford et al
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1371/journal.pgph.0000918
• Title of Journal or Parent Work: PLOS Glob Public Health
• Volume: 2
• Issue: 8
• Start Page: e0000918
• End Page: e0000918
• Grant/Funding Information: Funding from the Emory Global Health Institute supported preliminary data collection (AA, MGCM, MVOE, AT). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
• Keywords: AI; pathogens; drinking water
• Research Categories: Computer Science; Chemistry, Biochemistry; Health Sciences, Radiology

Abstract

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.