Our Work

Our goal is to enable MORE people suffering from parasitic infection in the developing world to be diagnosed and treated FASTER. Diagnosis is costly and requires specialists. We use machine learning-based image analysis and mobile phone microscopy to democratize the process, requiring minimal training to fight some of the world’s most devastating neglected tropical diseases.

The Challenge

More than two billion people — 1 in 4 people worldwide — are currently infected with parasitic worms that cause neglected tropical diseases.

Infection with parasitic worms (Helminth infection) is among the most prevalent afflictions of people who live in areas of poverty in the developing world.

Parasitic worm infections kill at least half a million people every year. However, mortality is not the only consequence — helminth infection leads to chronic sickness that severely decreases quality of life and perpetuates the cycle of poverty. The greatest helminth disease burden occurs in Sub-Saharan Africa and Asia.

Further reading: Helminth Infections: Soil-transmitted Helminth Infections and Schistosomiasis

Diagnosis is a Bottleneck to Rapid Treatment

Safe and effective treatments for helminth infection exist and have already been used to cure millions of people. Surprisingly, these treatments often cost as little as US $0.05 per person. However, a bottleneck to rapid treatment is that it is difficult to diagnose many people quickly.

Diagnosing helminth infection in the developing world uses microscopic analysis of patient stool samples to identify worm eggs. Thus, diagnosis traditionally requires expensive microscopes and highly trained microscopists to recognize parasitic worm eggs.

What if microscopes were less expensive and almost anyone could capture microscope images of potential parasite eggs and have them identified remotely?

Our Solution

We use automated image analysis powered by machine learning to facilitate diagnosis of parasitic worm infection in the developing world. We designed our tool to be coupled with inexpensive and highly mobile cell phone microscopes that can be operated by almost anyone. Parasite ID reduces the bottleneck to patient treatment by removing the need for highly trained microscopists and the expensive microscopes conventionally used in diagnosis.

We have created a highly sensitive and specific diagnostic tool by assembling the largest helminth egg image dataset of its kind and by implementing cutting edge deep learning (machine learning) techniques.