Our Approach

Malatec proof of concept in action

A fast, mobile and cost-effective malaria diagnosis should be possible, say three recent graduates of the master’s program in Applied Information and Data Science. They have set out to develop the Malatec smartphone application to detect malaria in combination with a 3D printed microscope. The detection of malaria using a microscope is still considered the gold-standard and most accurate diagnosis in a low income environment.

Nowadays to detect malaria, a doctor seeds a lab test in addition to the common malaria symptoms that may include headache, vomiting and fever. The current gold standard for these laboratory tests, is the microscopic analysis of the patient’s blood which is done by a trained technician. This involves counting the parasites and determining the correct type of the pathogen. Based on the test results, the appropriate drugs and therapies are administered by the doctor. 

Malaria is a global infectious disease, which is transmitted by mosquitoes and occurs mainly in tropical and subtropical regions. According to the WHO, an estimated 228 million cases of malaria occurred worldwide in 2018. 213 million of those cases occur on the African continent. This number of cases corresponds roughly to the population of Great Britain, Germany and France combined. The disease has a significant negative impact on the health of the affected population, the economy and the development of these regions.

Algorithm for malaria detection

A malaria infection can be diagnosed with two different methods. The first method is a rapid diagnostic test (RDT), which only provides information about whether you are infected or not. However, this method does not give any information about the stage of the infection. For the second method, a doctor takes a blood sample from the patient and either examines it under a microscope himself – or he sends it to a laboratory for diagnosis. In a so-called blood smear (blood sample on a glass slide) the malaria parasites are stained for better detection and then optically quantified. The main issue with the second method is that manual counting of infected blood cells is not only labor-intensive, but also requires appropriate training and continuous education of the specialists.

How would a malaria test on a smartphone work? Similar to a malaria detection in the laboratory, a blood smear is used. However, the parasites are not subsequently detected and quantified by a person using a light microscope, but are determined by an object detection algorithm based on a neural network.

An algorithm-based, mobile malaria diagnosis has many advantages over a conventional malaria rapid test. On the one hand, the central storage of patient data by the app’s diagnosis allows malaria outbreaks to be tracked regionally and geographically. This enables local health care facilities to prepare for this outbreak with drugs, nursing staff and materials. On the other hand, app-based malaria detection is more precise and can determine the exact number of parasites (plasmodia) and the type of pathogen (plasmodium type). This information supports the physician in the choice of therapy. The potential of this new method lies in the range of applications: with this mobile microscope hardware, new algorithms could be used in the future to diagnose other diseases, such as tropical diseases for which no reliable mobile detection methods are currently available.

Development of a prototype and next steps

In developing a Malatec prototype, the team relies on the support of various partners who want to use Data Science for a good cause. The course directors of the Master of Applied Information and Data Science at HSLU were able to support the Malatec team in purchasing malaria test blood samples. The Malatec advisory board consists of specialists in the field of science, business and humanitarian sectors who combine a wealth of technical and practical know-how. Malatec is currently looking for partners for financing the prototype as well as donors. Malatec is determined to develop a cost-effective, mobile and accurate malaria diagnosis and to bring it to the African market.


For the development of a prototype, the team depends on the support of various partners. 
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