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The World Health Organization (WHO) estimated that pathogens like Escherichia coli, primarily linked to food and water contamination, are associated with 485,000 deaths from diarrheal diseases annually, translating to a staggering worldwide economic loss of nearly 12 billion USD per annum. International organizations like the WHO and United Nations Children's Fund (UNICEF) have established related guidelines and criteria for pathogenic detection technologies and driving the search for innovative and efficient detection methods. This comprehensive review examines the trajectory of waterborne pathogenic bacteria detection technologies from traditional techniques, i.e., culture-based methods, to current detection methods including various forms of polymerase chain reaction (PCR) techniques qualitative real-time PCR, digital PCR, ELISA, loop-mediated isothermal amplification, next-generation sequencing (NGS) and to emerging techniques, i.e., biosensors and artificial intelligence (AI). The scope of the review paper focuses on waterborne pathogenic bacteria that are recognized as human pathogens, posing tangible threats to public health through waterborne. The detection techniques' merits, constraints, research gaps and future perspectives are critically discussed. Advancements in digital droplet PCR, NGS and biosensors have significantly improved sensitivity and specificity, revolutionizing pathogen detection. Additionally, the integration of artificial intelligence (AI) with these technologies has enhanced detection accuracy, enabling real-time analysis of large datasets. Molecular-based methods and biosensors show promise for efficient water quality monitoring, especially in resource-constrained settings, but on-site practical implementation remains a challenge. The pairwise comparison metrics used in this review also offer valuable insights into quick evaluation on the advantages, limitations and research gaps of various techniques, focusing on their applicability in field settings and timely analyses. Future research efforts should focus on developing robust, cost-effective and user-friendly techniques for routine waterborne bacteria monitoring, ultimately safeguarding global water supplies and public health, with AI and data analysis playing a crucial role in advancing these methods for a safer environment.
Oon et al. (Thu,) studied this question.