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Smartphone based image analysis for malaria diagnosis

Project

Smartphone based image analysis for malaria diagnosis

Project Details

Malaria is a major burden on global health with about 200 million cases worldwide, and 600,000 deaths per year. Inadequate diagnostics is a major barrier to effective management of cases and elimination of the disease. The current gold standard method for malaria diagnosis is light microscopy of blood films. About 170 million blood films are examined every year for malaria, which involves manually identifying and counting parasites. However, microscopic diagnostics are not standardized and depend heavily on the experience and skill of the microscopist, many of whom work in isolation, with no rigorous system in place for maintenance of their skills. For false negative cases this leads to incorrect diagnosis with unnecessary use of antibiotics, a second consultation, lost days of work, and in some cases progression into severe malaria. For false positive cases, this results in unnecessary use of antimalarial drugs and side effects.

 

To improve malaria diagnostics, the Lister Hill National Center for Biomedical Communications, an R&D division of the U.S. National Library of Medicine, NIH and Mahidol-Oxford Tropical Medicine Research Unit, University of Oxford, in Bangkok, Thailand are developing a fully automated low-cost system that uses a mobile phone and standard light microscope for parasite detection and counting on blood films. Compared to manual counting, automatic parasite counting is more reliable and standardized, reduces the workload of the malaria field workers and reduces diagnostic costs. To count parasites automatically, the system uses image processing methods to find cells infected with parasites in digitized images of blood films. The system is trained on manually annotated images and machine learning methods then discriminate between infected and uninfected cells, detect the type of parasites that are present, and perform the counting. The system uses a regular smartphone and digital images acquired on standard light microscopy equipment making it ideal for resource-poor settings.

 

This PhD project will develop and test this system for real-world use for malaria diagnosis. It will include optimisation of the system at NIH and testing of the system in the field at MORU including the smartphone application interface and performance, the system for connecting the smartphone to standard light microscopes, development of a core set of performance metrics for the application, field testing of the entire system for malaria diagnosis together with government healthcare workers and National Malaria Control Programme staff, structured interviews to gather feedback on the system and its potential role in malaria diagnosis in different settings, a formal field trial of the system performance and development of a system implementation guidance document for National Malaria Control Programmes.

 

The student will join a dynamic team of image analysis specialists at NLM and epidemiologists, modellers and clinicians at the MORU offices in Bangkok. They will spend time at field sites in malaria-endemic areas and will interact with government staff. Training will be provided at NIH on basic image analysis and smartphone application development and at MORU on malaria miscroscopy, clinical study methodology, data analysis and research ethics.

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