Spectroscopy method, paired with deep learning, diagnoses malaria quickly
Researchers at Duke University (Durham, NC) have devised a computerized method that involves spectroscopy to autonomously and quickly diagnose malaria infection with 97%-plus accuracy. The method could be used in resource-limited areas where infection rates are highest.
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While rapid diagnostic tests for malaria 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 patient's recovery. To overcome these hurdles, the research team used 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 most anyone and anywhere in the field.
"With this technique, the path is there to be able to process thousands of cells per minute," says Adam Wax, professor of biomedical engineering at Duke. "That's a huge improvement to the 40 minutes it currently takes a field technician to stain, prepare, and read a slide to personally look for infection."
The new 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 Hansang Park, a doctoral student in Wax's laboratory and first author on the paper that describes the work. 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," he says.
To get a more accurate reading, Wax and Park turned to deep learning—a method by which computers teach themselves how to distinguish between different objects. By feeding data on more than 1000 healthy and diseased cells into a computer, the deep learning program determined which sets of measurements at which thresholds most clearly distinguished healthy from diseased cells.
When they put the resulting algorithm to the test with hundreds of cells, it was able to correctly spot malaria 97–100% of the time—a number the researchers believe will increase as more cells are used to train the program. Because the technique breaks data-rich holograms down to just 23 numbers, tests can be easily transmitted in bulk, which is important for locations that often do not have reliable, fast Internet connections, and 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. They hope to show that a device based on this technology would be accurate and cost-efficient enough to be useful in the field. Wax has also received funding to begin exploring the use of the technique for spotting cancerous cells in blood samples.
Full details of the work appear in the journal PLOS One; for more information, please visit http://dx.doi.org/10.1371/journal.pone.0163045.