Duke University researchers have devised a computerized method to autonomously and quickly diagnose malaria with clinically relevant accuracy.

Malaria 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 not enough field workers and functioning microscopes to keep pace with the parasite.

In 2015, malaria infected 214 million people worldwide, killing an estimated 438,000. Image credit: Pixabay.Although rapid diagnostic tests exist, it is expensive to buy new kits. These tests also cannot tell how severe the infection is by tallying the number of infected cells, which is important for managing a patient’s recovery.

Now, engineers led by Adam Wax, professor of biomedical engineering, report a method that uses computer "deep learning" and light-based holographic scans to spot malaria-infected cells from a simple, untouched blood sample without any help from a human. The innovation could form the basis of a fast, reliable test that could be given by almost anyone, anywhere in the field.

The technique is based on a technology called quantitative phase spectroscopy. As a laser sweeps through the visible spectrum of light, sensors capture how each discrete light frequency interacts with a sample of blood. The resulting data captures a holographic image that provides a wide array of valuable information that can indicate a malarial infection.

“We identified 23 parameters that are statistically significant for spotting malaria,” says Han Sang Park, a doctoral student in Wax’s laboratory. For example, as the disease progresses, red blood cells decrease in volume, lose hemoglobin and deform as the parasite within grows larger. This affects features such as cell volume, perimeter, shape and center of mass.

“However, none of the parameters were reliable more than 90% of the time on their own, so we decided to use them all,” says Park.

“To be adopted, any new diagnostic device has to be just as reliable as a trained field worker with a microscope,” says Wax. “Otherwise, even with a 90% success rate, you’d still miss more than 20 million cases a year.”

To obtain a more accurate reading, Wax and Park turned to deep learning—a method by which computers teach themselves how to distinguish among different objects. By feeding data on more than 1,000 healthy and diseased cells into a computer, the program determined which sets of measurements at which thresholds most clearly distinguished healthy from diseased cells.

When researchers tested the resulting algorithm with hundreds of cells, it was able to correctly spot malaria over 97% of the time—a percentage the researchers say may increase as more cells are used to train the program. Because the technique breaks data-rich holograms down to 23 numbers, tests can be transmitted in bulk, which is important in places that often do not have fast, reliable internet connections. That, in turn, could eliminate the need for each location to have its own computer for processing.

Wax and Park are now looking to develop the technology into a diagnostic device through a startup company called M2 Photonics Innovations. Wax has also received funding to begin exploring the use of the technique for spotting cancerous cells in blood samples.