Clinical laboratory · 2026
The Transformative Impact of Deep Learning and Artificial Intelligence on Parasitic Disease Diagnosis.
СтатьяОбзорНазвание и аннотация: Перевод в очереди
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Ibrahim Mohamed N, Alanazi Hamad H, Azab Eman Fawzy El
Аннотация
BACKGROUND: Artificial intelligence (AI), particularly deep learning (DL), is transforming parasitic disease diagnosis by addressing challenges in accuracy and accessibility. Convolutional neural networks (CNNs) and machine learning (ML) offer rapid detection of pathogens causing malaria, leishmaniasis, and schistosomiasis, promising significant advancements in global health.
METHODS: This letter reviewed AI applications, focusing on CNNs and ML for detecting parasitic pathogens in clinical samples, imaging, and epidemiological data. The analysis highlights model efficacy, challenges such as data variability and bias, and the potential of AI integration with portable diagnostics in resource-constrained settings.
RESULTS: AI-driven diagnostics demonstrate superior sensitivity and specificity in identifying malaria, leishmaniasis, and schistosomiasis compared to conventional methods. However, data heterogeneity and algorithmic bias pose challenges. Combining AI with portable tools shows potential for improving diagnosis in endemic regions.
CONCLUSIONS: AI, particularly DL, holds transformative potential for parasitic disease diagnosis. Overcoming data and bias challenges is essential for ethical and equitable implementation. Collaborative efforts to integrate AI with portable diagnostics can enhance global health outcomes in endemic areas.
METHODS: This letter reviewed AI applications, focusing on CNNs and ML for detecting parasitic pathogens in clinical samples, imaging, and epidemiological data. The analysis highlights model efficacy, challenges such as data variability and bias, and the potential of AI integration with portable diagnostics in resource-constrained settings.
RESULTS: AI-driven diagnostics demonstrate superior sensitivity and specificity in identifying malaria, leishmaniasis, and schistosomiasis compared to conventional methods. However, data heterogeneity and algorithmic bias pose challenges. Combining AI with portable tools shows potential for improving diagnosis in endemic regions.
CONCLUSIONS: AI, particularly DL, holds transformative potential for parasitic disease diagnosis. Overcoming data and bias challenges is essential for ethical and equitable implementation. Collaborative efforts to integrate AI with portable diagnostics can enhance global health outcomes in endemic areas.