Artificial Intelligence Based Test Systems to Resist Waterborne Diseases by Early and Rapid Identification of Pathogens: A Review

Joy, Chethna; Sundar, G. Naveen; Narmadha, D.

doi:10.1007/s42979-022-01620-0

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…But for most people, access to safe drinking water remains a pipe dream. To successfully address this worldwide threat, optimal solutions built on cutting-edge deep learning and machine learning developments can be deployed [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

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Application of Artificial Intelligence in the Management of Drinking Water: A Narrative Review

Maroju,

Choudhari,

Shaikh

et al. 2023

Cureus

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Waterborne illnesses are a significant concern worldwide. The management of water resources can be facilitated by artificial intelligence (AI) with the help of data analytics, regression models, and algorithms. Achieving the Sustainable Development Goals (SDGs) of the 2030 Agenda for Sustainable Development of the United Nations depends on understanding, communicating, and measuring the value of water and incorporating it into decision-making. Various barriers are used from the source to the consumer to prevent microbiological contamination of drinking water sources or reduce contamination to levels safe for human health. Infrastructure development and capacity-building policies should be integrated with guidelines on applying AI to problems relating to water to ensure good development outcomes. Communities can live healthily with such technology if they can provide clean, economical, and sustainable water to the ecosystem as a whole. Quick and accurate identification of waterborne pathogens in drinking and recreational water sources is essential for treating and controlling the spread of water-related diseases, especially in resource-constrained situations. To ensure successful development outcomes, policies on infrastructure development and capacity building should be combined with those on applying AI to water-related problems. The primary focus of this study is the use of AI in managing drinking water and preventing waterborne illness.

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Section: Introductionmentioning

confidence: 99%

“…Identifying pathogens is consequently crucial for treating and preventing aquatic illnesses. A ground-breaking development in object detection has been made possible by the advent of machine learning and deep learning, both powered by AI [ 2 ]. The current state of water resources makes it clear that better management is required.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence in the Management of Drinking Water: A Narrative Review

Maroju,

Choudhari,

Shaikh

et al. 2023

Cureus

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Assessment of sachet water quality in Kumasi, Ghana

Boadi,

Saah,

Badu

et al. 2023

Discov Water

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This study assessed the physicochemical and microbiological quality of sachet drinking water samples in Kumasi, a major city in Ghana. Samples were collected from various sources within the city. Physicochemical properties, including pH, total dissolved solids, and concentrations of calcium, sodium, potassium, and magnesium ions, were analyzed following established protocols. Additionally, fluoride concentration was determined. The assessment criteria for water quality were based on the World Health Organization’s and the Ghana Standards Authority’s recommended standards for drinking water. The samples were also subjected to microbial analysis to detect the presence of E. coli and coliforms, and to evaluate microbial quality. The findings indicated that most physicochemical properties of the samples met the World Health Organization’s standards for safe drinking water, except for the slightly acidic pH. Total dissolved solids and the concentrations of calcium, sodium, potassium, fluoride and magnesium ions were within acceptable ranges. Strong positive correlations were observed among various physicochemical parameters of sachet water. However, microbial analysis revealed that 67% of the samples were contaminated with pathogenic microorganisms, including E. coli and coliforms, indicating poor microbiological quality. While sachet water samples generally meet physicochemical safety standards, addressing microbial quality is essential to ensure the safety of drinking water in Kumasi.

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AI-Aided Robotic Wide-Range Water Quality Monitoring System

Awwad,

Husseini,

Albasha

2024

Sustainability

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Waterborne illnesses lead to millions of fatalities worldwide each year, particularly in developing nations. In this paper, we introduce a comprehensive system designed for the autonomous early detection of viral outbreaks transmitted through water to ensure sustainable access to healthy water resources, especially in remote areas. The system utilizes an autonomous water quality monitoring setup consisting of an airborne water sample collector, an autonomous sample processor, and an artificial intelligence-aided microscopic detector for risk assessment. The proposed system replaces the time-consuming conventional monitoring protocol by automating sample collection, sample processing, and pathogen detection. Furthermore, it provides a safer processing method against the spillage of contaminated liquids and potential resultant aerosols during the heat fixation of specimens. A morphological image processing technique of light microscopic images is used to segment images, assisting in selecting a unified appropriate input segment size based on individual blob areas of different bacterial cultures. The dataset included harmful pathogenic bacteria (A. baumanii, E. coli, and P. aeruginosa) and harmless ones found in drinking water and wastewater (E. faecium, L. paracasei, and Micrococcus spp.). The segmented labeled dataset was used to train deep convolutional neural networks to automatically detect pathogens in microscopic images. To minimize prediction error, Bayesian optimization was applied to tune the hyperparameters of the networks’ architecture and training settings. Different convolutional networks were tested in accordance with different required output labels. The neural network used to classify bacterial cultures as harmful or harmless achieved an accuracy of 99.7%. The neural network used to identify the specific types of bacteria achieved a cumulative accuracy of 93.65%.

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Artificial Intelligence Based Test Systems to Resist Waterborne Diseases by Early and Rapid Identification of Pathogens: A Review

Cited by 3 publications

References 34 publications

Application of Artificial Intelligence in the Management of Drinking Water: A Narrative Review

Application of Artificial Intelligence in the Management of Drinking Water: A Narrative Review

Assessment of sachet water quality in Kumasi, Ghana

AI-Aided Robotic Wide-Range Water Quality Monitoring System

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