Holographic Imaging and Deep Learning Diagnose Malaria

Duke researchers have devised a computerized method to autonomously and quickly diagnose malaria with clinically relevant accuracy — a crucial step to successfully treating the disease and halting its spread. In 2015 alone, malaria infected 214 million people worldwide, killing an estimated 438,000. While Western medicine can spot malaria with near-perfect accuracy, it can be difficult to diagnose in resource-limited areas where infection rates are highest.   Malaria’s symptoms can look like many other diseases, and there are simply not enough well-trained field workers and functioning microscopes to keep pace with the parasite. While rapid diagnostic tests do exist, it is expensive to continuously purchase new tests. These tests also cannot tell how severe the infection is by tallying the number of infected cells, which is important for managing a…


Link to Full Article: Holographic Imaging and Deep Learning Diagnose Malaria