Mobile Phone Malaria Diagnosis to replace microscopic means in Uganda

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We are all aware of how stressing, tiresome and painful it is when you have to wait for several minutes before the doctor delivers your results after a malaria diagnosis in Uganda. But this seems to be fading out with the new Mobile Phone Malaria Diagnosis. With the new Mobile Phone Malaria Diagnosis, all these hustles are phased out by the first AI lab in Uganda.

How does the new Mobile Phone Malaria Diagnosis?

On the go, a doctor or any medical personnel responsible for blood samples clamps a Smartphone over one microscope eyepiece which views and magnifies a detailed image of the blood sample. With the inbuilt technology of this application, all malaria parasites shall be circled in red.

This innovation is an inspiration and was spearheaded by a PhD researcher, Rose Nakasi, 31 who claims that. “Almost everyone in Uganda, including me, has had malaria. It affects me as a person, and it affects Uganda. So I feel attached and want to contribute in any way that I can to its proper diagnosis” Nakasi is a researcher in computer science.

Malaria is the leading cause of death in Uganda as per statistics presented by the Ministry of Health in 2016, that is, 27 percent of deaths.

Mortality rates are particularly high in rural areas, where the lack of doctors and nurses is acute. Nursing assistants are often taught to read slides instead, but inadequate training can lead to misdiagnosis.

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Nakasi continues to say, “You have cases where someone goes to the hospital and is diagnosed negative, but after a few days they come back and there is malaria,”

Using Nakasi’s new Mobile Phone Malaria Diagnosis technology, pathogens are counted and mapped out quickly, ready to be confirmed by a health worker. Diagnosis times could be slashed from 30 minutes to as little as two minutes.

Whereas the base idea of her new Mobile Phone Malaria Diagnosis technology is not to take away technicians jobs, it primarily makes them easier. The technology has to work hand in hand with the lab technicians. Their expertise is needed to train the device and moving forward will make their work more efficient.

The AI software is built on deep learning algorithms that use an annotated library of microscope images to learn the common features of plasmodium parasites that cause malaria and the bacteria called Mycobacterium tuberculosis that is responsible for tuberculosis. (For those in Artificial intelligence field, and Data science)

The device is yet to be rolled out beyond small-scale trials in Kampala’s hospitals, but the biggest challenge may lie ahead as the technology is taken to remote areas.

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