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.
Automatic parasite counting has several advantages compared to manual counting:
It provides a more reliable and standardized interpretation of blood films.
It allows more patients to be served by reducing the workload of the malaria field workers.
And, it reduces diagnostic costs.
To count parasites automatically, the system uses image processing methods to find parasites in digitized images of blood films. The system first learns the typical shape and visual appearance of parasites based on manually-annotated training images. Machine learning methods then detect whether parasites are present, perform the counting, and discriminate between infected and uninfected cells. The system uses digital images acquired on standard light microscopy equipment, which makes it well-suited for resource-poor settings. The goal is to use inexpensive and highly portable smartphone technology to acquire blood film images in the field and to run the automated diagnostic system on these images. To achieve this goal, the HHS Ventures Fund is supporting the porting of our software to a smartphone platform. The Ventures Fund is a highly competitive effort that provides growth-stage funding, 15 months of mentoring, and management tools to support teams seeking to move their proven concepts to scale and create sustainable business models for their applications.
Extramural researchers on this project are Abhisheka Bansal (Jawaharlal Nehru University, India), Kamolrat Silamut (Mahidol University, Thailand), Richard Maude (University of Oxford, UK), Kannappan Palaniappan (University of Missouri), and Ilker Ersoy (University of Missouri). Datasets associated with this project are here.
In a separate project, we are developing a software application to automate parasitemia measurements in rodent malaria models for the preclinical testing of antimalarial drugs and vaccines.
Yang F, Yu H, Silamut K, Maude R, Jaeger S, Antani SK. Smartphone-Supported Malaria Diagnosis Based on Deep Learning. Proceedings of 10th Workshop on Machine Learning in Medical Imaging (MLMI 2019) in conjunction with MICCAI, Shenzhen, China, Oct 13-17, 2019.
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.