To eliminate malaria, scalable tools that are rapid, affordable, and can detect patients with low parasitemia are required. Non-invasive diagnostic tools that are rapid, reagent-free, and affordable would also provide a justifiable platform for testing malaria in asymptomatic patients. However, non-invasive surveillance techniques for malaria remain a diagnostic gap. Here, we show near-infrared Plasmodium absorption peaks acquired non-invasively through the skin using a miniaturized hand-held near-infrared spectrometer. Using spectra from the ear, these absorption peaks and machine learning techniques enabled non-invasive detection of malaria-infected human subjects with varying parasitemia levels in less than 10 s.
© The Author(s) 2022. Published by Oxford University Press on behalf of the National Academy of Sciences.