Scientists have developed a groundbreaking computational system that automates the detection and counting of Leishmania parasites in microscopy images, potentially revolutionizing the way tropical diseases are studied and treated[1].
Leishmaniasis, a devastating tropical disease affecting millions worldwide with up to 30,000 deaths annually, has long posed challenges for researchers due to the time-consuming process of manually counting parasites under microscopes[1].
The new system employs advanced image processing techniques and machine learning to automatically identify and count the parasites within infected cells. Using a combination of sophisticated algorithms, the system achieved an impressive 93.3% accuracy rate in detecting parasites[1].
This technological advancement is particularly significant for laboratories in developing countries, where expensive high-content screening systems are often unavailable. The system works with standard microscopy equipment and Giemsa staining, making it accessible to facilities with limited resources[1].
The research team, comprised of scientists from multiple Cuban institutions, focused on creating a solution that could effectively separate and count overlapping parasites, a common challenge in microscopic analysis. Their method successfully processes large volumes of images, significantly reducing the time needed for analysis while maintaining high accuracy[1].
This development represents a significant step forward in the fight against leishmaniasis, potentially accelerating the discovery of new treatments by streamlining the drug screening process[1].
Quellen:
[1] full https://www.frontiersin.org/journals/medical-technology/articles/10.3389/fmedt.2024.1360280/full
[2] Frontiers | Detection and counting of Leishmania intracellular parasites in microscopy images https://www.frontiersin.org/journals/medical-technology/articles/10.3389/fmedt.2024.1360280/full
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