More about the issue
The Problem
Malaria is one of the deadliest diseases in the world, Each year it puts millions of lives at risk. In 2023, there were approximately 263 million cases, resulting in 597,000 deaths (according to the WHO). The burden is most severe in Africa, accounting for 94% of cases and 95% of deaths globally. Despite existing preventive measures like bed nets, vaccines, and medications, malaria remains a deadly threat.
The Solution with AI
AI-based malaria detection has the potential to address these limitations by analyzing more samples in less time. This ensures that malaria diagnostics are faster, more accurate detection and enables healthcare workers to focus more on treatment of the patients.
The Challenge
Early detection and treatment are essential in combating malaria. Currently, diagnosing malaria requires microscopes and skilled technicians, resources often limited in high-burden areas such as Uganda and Ghana. Additionally, even trained technicians can only examine about 30 samples per day, limiting the capacity for timely diagnoses in these regions.
The Malaria Cycle
Malaria parasites are transmitted through bites from female Anopheles mosquitoes. These bites release the parasites into the bloodstream, which travel to the liver to multiply. The parasites then enter red blood cells, grow, and cause the cells to burst, releasing even more parasites and causing the cycle to continue.
When an infected person is bitten by another mosquito, the parasite is picked up and grows within the mosquito. In a few days it is ready to be passed on to another person through the next bite.
Symptoms
Malaria symptoms vary widely:
Mild cases: Fever, headache, and fatigue.
Severe cases: Exhaustion, breathing difficulties, unconsciousness, and, in some instances, death.
Who Does This Help?
Impact on Healthcare Systems:
An AI model detecting malaria allows healthcare workers to diagnose malaria faster. By automating the detection process, doctors can reduce diagnostic time and thus create more time on the actual treatment of the patient. Improving the overall efficiency of the healthcare system.
Impact on Patients:
With this AI model, malaria can be diagnosed at its earliest stage, enabling early treatment and helping prevent severe symptoms and possibly death.
Impact on Malaria Transmission:
By diagnosing and treating malaria at its earliest stages, the AI model reduces the risk of transmission. This helps control the spread of the disease, contributing to long-term efforts to eradicate malaria.
SDG
This initiative aligns with the United Nations Sustainable Development Goal #3: Good Health and Well-Being. By participating, we contribute to improving healthcare service, especially in underserved regions.