Project Summary
Malaria is one of the leading health problems of the developing world. Malaria endemicity has
been attributed to poor diagnosis at the lab level. This quite often leads to disease misdiagnosis
and drug resistance. Many developing countries are faced with a lack of critical mass of lab
technicians to diagnose the disease through a gold standard mechanism of microscopy and this
has worsened the already dire situation in some of these Countries. World over the trending
technologies are now based on machine learning and deep learning techniques. These can be
leveraged with the combination of smartphones to improve disease diagnosis. However, most of
the previous work on automation for microscopy diagnosis has been carried out adhocly in the
lab environment and no study seems to give a practical field deployable solution. The goal of the
proposed research is to develop a rapid, low cost, accurate and simple in-field screening system
for microscopy challenges like malaria. Specifically, this study will test and validate developed
image analysis models for real time field-based diagnostics and surveillance of malaria. The
proposed solution builds from our earlier work on mobile microscopy carried out at Makerere
University AI Lab, that has confronted automated microscopy through exploiting recent
technological advances in 3D printing to enable development of a low-cost 3D printed adapter.
This has enabled attachment of a wide range of Smartphones on a microscope, furthermore, we
have implemented deep learning models for pathogen detection to produce effective hardware
and software respectively.
The software component of our work is to train machine learning methods to recognise different
pathogen objects. The diagnosis solutions have however been ad-hoc in its current state where
different conditional settings like image scaling, phone resolutions and grid readings were not
standardized and therefore poor performance of the model when deployed in field testing. Our
Infield automated screening trials will therefore involve achieving robust outcomes, through 1)
Development of machine learning approaches for standardised field-based microscopy of
malaria diagnosis in Uganda. 2) Building a complementary framework for real time
surveillance and improved diagnosis of malaria platform through in-field diagnostic
studies. The point-of-care field-based diagnostic system proposed here addresses a major
unmet public health malaria screening and surveillance need to reliably inform public health
interventions in malaria control and prevention.