Advanced microscope designer and manufacturer Motic has partnered with the Global Good Fund to create and distribute the EasyScan GO, which is a microscope equipped with artificial intelligence technology that will be used to identify and count malaria parasites.

As part of the collaboration, the software from Global Good—a collaboration between Intellectual Ventures and Bill Gates to develop technologies for humanitarian impact—is integrated into an existing Motic microscope to be used for disease scanning.

Malaria kills almost half a million people each year, and researchers estimate that nearly half the world’s population is at risk of contracting it, according to Motic. Accurate detection of severe and drug-resistant cases requires analysis of a blood smear by a World Health Organization-certified expert microscopist, which takes roughly 20 minutes per slide. By automating the process, this can alleviate the shortfall of trained personnel in under-resourced countries.

Based on machine learning and neural networking, EasyScan GO’s software module is trained by feeding it thousands of blood smear slides annotated by experts. The microscope works through a combination of digital slide scanning and the software module, which runs captured images through a machine learning algorithm for counting and detection.  Field tests of an early prototype of the microscope presented at the Conference on Computer Vision (ICCV) showed that the machine learning algorithm developed by Global Good is as reliable as an expert microscopist, according to Motic.

"Our goal in integrating Global Good’s advanced software into Motic’s high-quality, affordable digital slide scanner is to simplify and standardize malaria detection," said Richard Yeung, Vice President of Motic China. "Success with the most difficult-to-identify disease paves the way for the EasyScan product line to excel at almost any microscopy task and to detect other major diseases that affect developed and emerging markets alike."

Now, the EasyScan GO is being trained to recognize all species of malaria and other parasites and traits commonly found on a blood film, including Chagas disease, microfilaria, and sickle cell. Additionally, the team will reportedly explore its application to other sample types, such as sputum, feces and tissue, as well some forms of cancer.