Diversity of Giardia intestinalis: Comparative evaluation of AI stool analysis, traditional techniques, and molecular phylogeny
DOI:
https://doi.org/10.5281/zenodo.17386580Keywords:
AI stool analyzer, Molecular diagnostics, Conventional techniquesAbstract
Giardia intestinalis is one of the most prevalent intestinal protozoan parasites causing Giardiasis, a disease infecting diverse people of any age and characterized by abdominal cramps, diarrhea, and weight loss. Although traditional diagnostic techniques are still widespread, however, newer AI and molecular technologies have much to offer in the way of more precise and quicker detection. The aim of this research was to determine the prevalence of Giardiasis among patients presenting the Public Health Laboratory in Erbil, Iraq, between January–December 2024, and to create a comparison of the effectiveness of traditional, immunological, molecular, and AI-based diagnostic techniques. A total of 25,460 stool specimens were first screened by direct wet mount microscopy. Positives were further analyzed with trichrome and acid-fast stains for morphological identification. Immunochromatographic assays and the KU-F600 AI-based automatic fecal analyzer were utilized. Molecular detection by real-time PCR and standard DNA sequencing was utilized for G. intestinalis infection confirmation. The diversity result of G. intestinalis was evaluated using direct wet mount, revealed 290 positive cases, which was represented 1.14% infection rate. Immunochromatographic and AI-based methods significantly enhanced diagnostic speed and ease compared to conventional microscopy. Molecular techniques (real-time PCR and sequencing) demonstrated the highest accuracy in detecting G. intestinalis. Although traditional microscopy remains a useful screening tool, it is less reliable than molecular and AI-based methods. The KU-F600 AI analyzer exhibited strong potential for rapid and accurate diagnosis. Further research is recommended to validate the broader application of AI technologies in parasitological diagnostics.
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