Harnessing artificial intelligence microscopy to improve diagnostics for soil-transmitted helminthiasis and schistosomiasis: a review of recent advances and future pathways.

Autor: Ward P; Enaiblers, Uppsala, Sweden.; IDLab, Ghent University - Imec, Zwijnaarde.; Department of Translational Physiology, Infectiology and Public Health, Ghent University, Merelbeke, Belgium., Levecke B; Department of Translational Physiology, Infectiology and Public Health, Ghent University, Merelbeke, Belgium., Ajjampur S; Sitara Ajjampur, The Wellcome Trust Research Laboratory, Christian Medical College Vellore, Vellore, Tamil Nadu, India.
Jazyk: angličtina
Zdroj: Current opinion in infectious diseases [Curr Opin Infect Dis] 2024 Oct 01; Vol. 37 (5), pp. 376-384. Date of Electronic Publication: 2024 Aug 02.
DOI: 10.1097/QCO.0000000000001048
Abstrakt: Purpose of Review: This opinion piece aims to explore the transformative potential of integrating artificial intelligence with digital microscopy to enhance diagnostics for soil-transmitted helminthiasis (STH) and schistosomiasis (SCH), two pervasive neglected tropical diseases (NTDs). By aligning innovative artificial intelligence-driven solutions with WHO's strategic objectives and calls for better, more accessible, and more integrated diagnostics, we highlight the latest advancements that may support improved health outcomes in affected communities.
Recent Findings: The review covers recent advancements in artificial intelligence-based diagnostic technologies, emphasizing automated egg detection and quantification. These technologies promise to mitigate challenges such as human error and the need for skilled technicians.
Summary: The findings have significant implications for public health, ethical considerations and regulatory pathways, particularly in resource-limited settings. The authors advocate for interdisciplinary collaboration and a strategic focus on meeting WHO target product profiles to ensure uptake, ultimately to support reaching WHO NTD targets.
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Databáze: MEDLINE