EasyScan GO: an AI powered malaria parasite counter

20th November 2017

Enaie Azambuja

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Datasheet

EasyScan GO, an AI powered microscope developed by Chinese manufacturer Motic, has the capability to automatically and accurately quantify malaria parasites in a blood sample. Utilising machine learning algorithms, the microscope is so efficient that it can identify the amount of parasites present in under 20 minutes.

Premiered at the International Conference on Computer Vision, the AI equipped microscope can quantify malaria parasites on par with experts, surpassing the capabilities necessary to be certified by the World Health Organisation for Competency 1 microscope.

The project was a joint effort between Motic and Global Good, a partnership between Intellectual Ventures, a giant patent holder, and Bill Gates. “Our goal in integrating Global Good’s advanced software into Motic’s high-quality, affordable digital slide scanner is to simplify and standardise malaria detection,” said Richard Yeung, Vice President of Motic China.

EasyScan GO works through a combination of digital slide scanning and a diagnostic software module based on machine learning and neural networking. The machine was trained by feeding it thousands of blood smear slides annotated by experts.

These images were processed through a machine learning algorithm, followed by a field tests which was published and premiered at the Conference on Computer Vision.

Malaria affects 200 million people a year, and 400,000 of those cases are fatal. Typically found in tropical and subtropical countries, strains of malaria are becoming resistant to our most effective drugs.

Though physicians do have rapid diagnostic tests at their disposal, physicians and researchers depend on microscopes, like the EasyScan GO, to quantify the amount and strain of malaria present in a sample.

EasyScan GO makes monitoring drug effectiveness more efficient, freeing up expert microscopists as well as making the ability to quantify malarial strains available to the wider medical community.

“Malaria is one of the hardest diseases to identify on a microscope slide,” said David Bell, Director of Global Health Technologies supporting Global Good. “By putting machine learning-enabled microscopes in the hands of laboratory technicians, we can overcome two major barriers to combating the mutating parasite—improving diagnosis in case management and standardising detection across geographies and time.”