Machine-Learning Microscope Speeds Malaria Diagnosis

U.S. and German engineers have developed a microscope that uses machine learning to co-optimize illumination from a bank of 1,500 programmable LEDs and image classification, for faster diagnosis of malaria. Nearly half the world's population is at risk of malaria, with an estimated 219 million cases of the mosquito-borne disease in 2017 alone, according to the World Health Organization. Early and accurate diagnosis of malaria allows clinicians to treat infected individuals quickly, which increases their chances of survival and prevents further transmission of the disease. Researchers in the United States and Germany have now brought optics and machine learning together in a bid to accelerate the disease's diagnosis (Biomed. The most common diagnostic test for malaria involves examining a drop of blood under an optical microscope to check for the presence of Plasmodium falciparum, a single-celled parasite that causes malaria.