New 'man, machine' system provides for improved malaria diagnosis
Researchers have found a new 'man and machine' decision support system, which provides more effective diagnosis for malaria than the traditional methods.
A Finnish-Swedish research group at the Institute for Molecular Medicine Finland (FIMM), University of Helsinki, and Karolinska institutet, Stockholm have developed this method that has been based on computer vision algorithms similar to those used in facial recognition systems combined with visualization of only the diagnostically most relevant areas. Tablet computers can be utilized in viewing the images.
In this newly developed method, a thin layer of blood smeared on a microscope slide was first digitized. The algorithm analyzed more than 50,000 red blood cells per sample and ranks them according to the probability of infection. Then the program created a panel containing images of more than a hundred most likely infected cells and presented that panel to the user. The final diagnosis was done by a health-care professional based on the visualized images.
Research Director Johan Lundin (MD, PhD) from the Institute for Molecular Medicine Finland, FIMM, said that they were not suggesting that the whole malaria diagnostic process could or should be automated but their aim was to develop methods have a potential to considerably increase the throughput in malaria diagnostics.
The developed support system can be applied in various other fields of medicine. In addition to other infectious diseases such as tuberculosis, the research group is planning to test the system fro cancer diagnostics in tissue samples.
Professor Vinod Diwan from Karolinska Institutet, said that the new method of imaging and analysis could revolutionise the point of care diagnostics of not only malaria but also several diseases where diagnosis depends on microscopy.
The research is published in PLOS One scientific journal.
(Posted on 22-08-2014)
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