Diagnostic performance of the miLab MAL AI-driven automated digital microscopy system versus standard microscopy for <i>Plasmodium</i> spp. detection

AI microscope spots malaria parasites accurately in 162 patient samples but misses some species

Researchers in France prospectively tested an AI-assisted digital microscopy system, miLab MAL, on thin blood smears from patients with suspected malaria or post-treatment follow-up. Over six months, 162 samples were compared against conventional diagnostic methods.

The gain The AI-assisted microscope detected malaria parasites with 98.7% specificity and 90.4% sensitivity against LAMP and/or thick smear, rising to 97.4% sensitivity against thin smear alone.

The problem The system misidentified some parasite species, failing to correctly name 11 P. falciparum and mistaking two P. ovale and one P. malariae for P. vivax, and it tended to underestimate parasitemia.

Journal of Clinical Microbiology
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