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Artificial Intelligence Offers a Better Way to Diagnose Malaria

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The Autoscope uses deep-learning software to quantify the malaria parasites in a sample.

The Autoscope automated microscope with artificial intelligence is 90% accurate and specific at detecting malaria parasites.

Credit: Intellectual Ventures Laboratory

Intellectual Ventures Laboratory researchers have developed Autoscope, an automated microscope equipped with artificial intelligence that is 90-percent accurate and specific at detecting malaria parasites.

The researchers tested the Autoscope in the field at the Shoklo Malaria Research Unit on the Thailand-Myanmar border during malaria season in December 2014 and January 2015.

The researchers trained the Autoscope's deep-learning software on 120 slides from collections around the world, both with and without malaria. The software uses visual features such as shape, color, and texture to calculate the probability a given object is a malaria parasite. The software was able to classify 170 samples during the field testing in Thailand.

"It could have broad applicability, not only in research and surveillance of antimalarial drug resistance but also in clinical practice," says the WorldWide Antimalarial Resistance Network's Mehul Dhorda.

The microscopy currently used to quantify the parasites requires well-trained microscopists, who are in short supply in many malaria-prone areas.

"We're not as good as the very best humans, but we're certainly better than almost all microscopists in the field," based on the World Health Organization's standards, says Intellectual Ventures Laboratory researcher Charles Delahunt.

The Autoscope system was built with support from Bill and Melinda Gates through the Global Good Fund.

From Technology Review
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