Researchers at Duke University have devised a computerized method for autonomous, rapid diagnosis of malaria (comparable to the accuracy of clinical diagnosis), a crucial step in successfully diagnosing the disease and preventing its spread.
In 2015 alone, the number of malaria infections worldwide reached 214 million, and the death toll was estimated at 438,000.
Although Western medicine can diagnose malaria with very high accuracy, in poor areas, malaria diagnosis is very difficult due to limited resources, resulting in a high rate of malaria infection in these areas.
The symptoms of malaria are very similar to many other types of diseases, and relying only on experienced staff and microscopes is not enough to make an accurate judgment. Although there is a rapid diagnostic method, it is necessary to continuously purchase new test papers, resulting in an expensive price. Moreover, these tests cannot tell you the severity of the infection by counting the number of infected cells, and these data are very important for the patient's recovery.
In a new study, engineers at Duke University introduced a new approach to using computer “deep learning†and light-based, holographic scanning methods to track malaria-infected cells, and this is an autonomous approach that requires only simple, No contact detection. This innovation lays the foundation for fast, reliable testing, costing $2.7 billion annually to fight malaria worldwide, so this technology is invaluable.
The results were published online in the September 16 issue of the journal PLOS ONE.
"With this technology, you can process thousands of cells per minute," said Adam Wicks, a professor of biomedical engineering at Duke University. “This is a huge improvement, and it takes 40 minutes for field technicians to perform a series of preparations and diagnoses.â€
This new technology was developed on the basis of quantitative phase spectroscopy. Laser scanning through the spectrum of visible light captures how each discrete optical frequency interacts with the blood sample. The data generated by holographic images provides a wealth of valuable information that can be used to diagnose malaria infections.
“We identified 23 statistically significant parameters for the diagnosis of malaria,†said Han Sang Park, Ph.D. at Wexs Laboratories and the first author of the paper. For example, as the disease progresses, the number of red blood cells decreases, hemoglobin decreases, and as the parasite becomes larger, red blood cells are deformed. These are all infection characteristics such as cell number, circumference, shape and quality.
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