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A novel 'man and machine' decision support system makes malaria diagnostics more effective
Date:8/21/2014

A Finnish-Swedish research group at the Institute for Molecular Medicine Finland (FIMM), University of Helsinki, and Karolinska institutet, Stockholm, has developed a novel "man and machine" decision support system for diagnosing malaria infection. This innovative diagnostic aid was described in PLOS One scientific journal today, 21 August. The method is 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 is first digitized. The algorithm analyzes more than 50,000 red blood cells per sample and ranks them according to the probability of infection. Then the program creates a panel containing images of more than a hundred most likely infected cells and presents that panel to the user. The final diagnosis is done by a health-care professional based on the visualized images.

By utilizing a set of existing, already diagnosed samples, the researchers were able to show that the accuracy of this method was comparable to the quality criteria defined by the World Health Organization. In the test setting, more than 90% of the infected samples were accurately diagnosed based on the panel. The few problematic samples were of low quality and in a true diagnostic setting would have led to further analyses.

"We are not suggesting that the whole malaria diagnostic process could or should be automated. Rather, our aim is to develop methods that are significantly less labor intensive than the traditional ones and have a potential to considerably increase the throughput in malaria diagnostics", said Research Director Johan Lundin (MD, PhD) from the Institute for Molecular Medicine Finland, FIMM.

"The equipment needed for digitization of the samples is a challenge in developed count
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Contact: Dr. Nina Linder
nina.linder@helsinki.fi
358-445-555-407
University of Helsinki
Source:Eurekalert  

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A novel 'man and machine' decision support system makes malaria diagnostics more effective
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