Optical Smart Malaria Diagnostics (OSMD)


Malaria is a life threatening disease prevalent in tropical and subtropical countries with high mortality and significant economic loss. Based on World Health Organization (WHO) report, 429,000 death cases  were attributed to Malaria in the year 2015. 212 million new cases of malaria  worldwide was reported in the same year and about 3.2 billion people remain at risk of malaria globally.

Human operator analysis of peripheral blood smear is considered the “gold standard”  of  malaria diagnostic,   however  in sub-Saharan Africa and other developing countries the performance of this approach  is limited by the lack of educated personnel and equipment, resulting in  week-long outcome delays causing human life losses. Our research therefore, focuses on the development of cost-effective optical methods  and diagnostic tools for early malaria detection based on optical analysis of stained and unstained blood smears. 

Since the traditional white light microscopy is insensitive to unstained samples, we are also  looking for a method that combines independent microscopic imaging modalities. These will include multispectral imaging, polarization imaging and scattering analysis. Each modality is represented by a separate microscopic image or a hologram, obtained at certain wavelength, or certain polarization state of illumination light.

Even if the probability of correct diagnosis is low within a single modality, the combined probability can be quite high for a combination of modalities, according to the Bayes rule, especially if a reasonably large numbers of imaging modalities is used for analysis.
Finally, by tuning the diagnostic method for highest sensitivity at each modality, we hope to obtain a recipe that is tuned for reliable automated optical diagnosis of malaria parasite in an unstained blood smear, with minimal human involvement.

Goal/ objective

Our goals consists in the development of:

  1. An automated, simple, low-cost,  easy to use and field deployable diagnostic instrument  for detecting malaria parasite in stained  peripheral blood smear.

  2. An automated robust detection of malaria parasite in an unstained blood smear using a multi-modal optical method, based on the combination of multispectral microscopy, polarization-sensitive imaging and digital holography.

  3. Point of care diagnostics in low-resource setting in Sub-Saharan Africa thereby reducing mortality rate due to malaria infection among children and pregnant women.

  4. Business models that will focus on developing OSMD at very affordable cost for deployment and use in most primary health care centres in malaria endemic countries.

  5. Low-cost optical instruments for the diagnosis  of Schistosomiasis infection  (a neglected tropical diseases) prevalent in resource limited countries.


Work programme

    1. Simplification and miniaturisation of current gold standard Light microscopy : In this phase, we designed and develop a cell-phone based microscope for malaria parasite detection in peripheral blood smear.  A 3-D printed prototype (Excelscope 2.0) is being developed with close collaboration with TU Delft Industrial Design Engineering and Flexible Optical B.V using  15,000 Euros Delft Global seed fund won by our consortium in Global Health Pressure Cooker challenge organised by Delft Global Initiative (31st January 2017).

    2. Design and development of multimodal imaging techniques and algorithms: In this phase we will explore other imaging modalities that will enable the easy detection of Plasmodium Falciparum in unstained blood smear.

    3. Diagnosis of  Neglected Tropical Diseases : In our interactions and collaboration with our Leiden partners, we decided to explore the low-cost methods for the diagnosis of Schistosomiasis in low resource settings. This neglected tropical disease accounts for about 200,000 deaths annually and yet it receives very little control attention.  To contribute to the extermination of these NTDs we will be designing and developing low-cost and sensitive diagnostic instruments for the detection of Schistosoma haematobium.

    4. Demonstration and validation of prototypes: We will validate the instrument with real patients on the field in close collaboration with our partner in College of Medicine, University of Ibadan, Nigeria. 


    • Taking advantage of the low-cost cell-phone with high pixel resolution sensors, advances in low-power light-emitting diodes (LEDs) and 3-D printing  technologies, a  battery powered smart-phone based platform has been developed for field use.  Our diagnostic instrument provides images with  the morphology of the parasite  at the early ring trophozoites and other mature stages of the parasite's developmental cycle. This device can be used for diagnosis in low resource settings where high-cost and bulky  diagnostic instruments (suitable only for clinical settings)  are scarcely available. Developed prototype will be practically  tested in Nigeria in the last quarter of the year 2017.

    •  Algorithms for automated parasite detection and evaluation of parasitemia in infected blood cell images captured with the smart-phone microscope  is currently being developed for system  integration.

    • In close collaboration with Leiden University Medical Centre,  a low-cost, sensitive diagnostic instrument for the detection of Schistosoma haematobium in urine sample is currently being developed and will be tested on the field



    Malaria prevention, early malaria diagnostics, microscopy, imaging, plasmodium falciparum, optical blood test, malarial blood test, holography, neglected tropical disease.

    Sponsored by:

    TU Delft Global Initiative a program of Delft University of Technology to boost science and technology for global development.


    1. Prof. Maria Yazdanbakhsh (Parasitology and Leiden Immunologyparasitology Group, Leiden University Medical Center, Leiden , The Netherlands.)

    2. Prof.Oladimeji Oladepo,  College of Medicine, University of Ibadan, Nigeria, West Africa.

    3. Flexible Optical B.V. Rijswijk ZH, The Netherlands.