Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

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dc.contributor.authorKittichai, Veerayuth
dc.contributor.authorKaewthamasorn, Morakot
dc.contributor.authorThanee, Suchansa
dc.contributor.authorSasisaowapak, Thanyathep
dc.contributor.authorNaing, Kaung Myat
dc.contributor.authorJomtarak, Rangsan
dc.contributor.authorTongloy, Teerawat
dc.contributor.authorChuwongin, Santhad
dc.contributor.authorBoonsang, Siridech
dc.date.accessioned2026-08-06T10:42:22Z
dc.date.available2026-08-06T10:42:22Z
dc.date.issued2023-10-01
dc.description.abstractTrypanosomiasis is a significant public health problem in several regions across the world, including South Asia and Southeast Asia. The identification of hotspot areas under active surveillance is a fundamental procedure for controlling disease transmission. Microscopic examination is a commonly used diagnostic method. It is, nevertheless, primarily reliant on skilled and experienced personnel. To address this issue, an artificial intelligence (AI) program was introduced that makes use of a hybrid deep learning technique of object identification and object classification neural network backbones on the in-house low-code AI platform (CiRA CORE). The program can identify and classify the protozoan trypanosome species, namely Trypanosoma cruzi, T. brucei, and T. evansi, from oil-immersion microscopic images. The AI program utilizes pattern recognition to observe and analyze multiple protozoa within a single blood sample and highlights the nucleus and kinetoplast of each parasite as specific characteristic features using an attention map. To assess the AI program's performance, two unique modules are created that provide a variety of statistical measures such as accuracy, recall, specificity, precision, F1 score, misclassification rate, receiver operating characteristics (ROC) curves, and precision versus recall (PR) curves. The assessment findings show that the AI algorithm is effective at identifying and categorizing parasites. By delivering a speedy, automated, and accurate screening tool, this technology has the potential to transform disease surveillance and control. It could also assist local officials in making more informed decisions on disease transmission-blocking strategies.
dc.identifier.citationJournal of Visualized Experiments, 2023(200), 2023
dc.identifier.doi10.3791/65557
dc.identifier.issn1940087X
dc.identifier.other2-s2.0-85176259051
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/14649
dc.sourceJournal of Visualized Experiments
dc.titleSuperior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model
dc.typeArticle

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