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Malaria Screener

Project information

Malaria is caused by parasites that are transmitted through the bites of infected mosquitoes. With about 200 million cases worldwide, and about 400,000 deaths per year, malaria is a major burden on global health. Most deaths occur among children in Africa, where a child dies almost every minute from malaria, and where malaria is a leading cause of childhood neuro-disability. Typical symptoms of malaria include fever, fatigue, headaches, and in severe cases seizures, coma, and death.

While existing drugs make malaria a curable disease, inadequate diagnostics and emerging drug resistance are major barriers to successful mortality reduction. The development of a fast and reliable diagnostic test is therefore one of the most promising ways of fighting malaria, together with better treatment, development of new malaria vaccines, and mosquito control.

The current standard method for malaria diagnosis in the field is light microscopy of blood films. About 170 million blood films are examined every year for malaria, which involves manual counting of parasites.

Accurate parasite counts are essential to diagnosing malaria correctly, testing for drug-resistance, measuring drug-effectiveness, and classifying disease severity. However, microscopic diagnostics is not standardized and depends heavily on the experience and skill of the microscopist. It is common for microscopists in low-resource settings to work in isolation, with no rigorous system in place that can ensure the maintenance of their skills and thus diagnostic quality. This leads to incorrect diagnostic decisions in the field. For false negative cases, this means unnecessary use of antibiotics, a second consultation, lost days of work, and in some cases progression into severe malaria. For false positive cases, a misdiagnosis entails unnecessary use of anti-malaria drugs and suffering from their potential side-effects, such as nausea, abdominal pain, diarrhea, and sometimes severe complications.

To improve malaria diagnostics, the Lister Hill National Center for Biomedical Communications, an R&D division of the US National Library of Medicine, in collaboration with NIH’s National Institute of Allergy and Infectious Diseases (NIAID) and Mahidol-Oxford University, is developing a fully-automated system for parasite detection and counting in blood films.

Rajaraman S, Antani SK, Jaeger S. Visualizing Deep Learning Activations for Improved Malaria Cell Classification. Proceedings of The First Workshop in Medical Informatics and Healthcare (MIH 2017), Proceedings of Machine Learning Research (PMLR), v. 69, p. 40-47.
Jaeger S, Silamut K, Yu H, Poostchi Mohammadabadi M, Ersoy I, Powell A, Liang Z, Hossain M, Antani SK, Palaniappan K, Maude R, Thoma GR. Reducing the Diagnostic Burden of Malaria Using Microscopy Image Analysis and Machine Learning in the Field. Annual Meeting of the American Society of Tropical Medicine & Hygiene (ASTMH), Atlanta, USA, 2016.
Liang Z, Powell A, Ersoy I, Poostchi M, Silamut K, Palaniappan K, Guo P, Hossain M, Antani SK, Maude R, Huang J, Jaeger S, Thoma GR. CNN-Based Image Analysis for Malaria Diagnosis. IEEE International Conference on Bioinformatics & Biomedicine (BIBM), Shenzhen, China, 2016.
Gordon, E, Ersoy, I, Jaeger S, Waisberg, M, Pena, M, Thoma GR, Antani SK, Pierce, S, Palaniappan, K. Retinal Microcirculation Dynamics During an Active Malarial Infection. Annual Meeting of the American Society of Tropical Medicine and Hygiene (ASTMH), 2014.
Gordon E, Waisberg M, Ersoy I, Pena M, Jaeger S, Pierce S. The eye as a window to investigate the CNS microvasculature during a dynamic malaria infection. Third Annual Seminar on Molecular Imaging of Infectious Diseases, September 23, 2013.