Sirish Shah tells how his team developed software sensors to help industries ranging from polymers, and pulp and paper, to oil sands. They found the same image-based sensor technology was able to identify malaria in blood cells.
Research can be both transformative and serendipitous.
As an engineering professor at the University of Alberta, I lead a team that has developed software sensors that have found use in industries ranging from polymers, and pulp and paper, to oil sands. Most recently, we developed a sensor based on image-processing ideas to significantly reduce bitumen losses in oil sands tailings ponds.
At a wedding reception a few years back, I was seated next to a doctor who was concentrating his efforts on malaria — a disease that claims nearly one million lives worldwide each year. During our conversation, we wondered if we could use the same image-based sensor technology to identify malaria in blood cells. As someone who was born and grew up in Africa, I was motivated. Current strategies to detect and diagnose malaria are prone to human error, require manual assessment and are time-consuming and difficult to deploy.
Our hunch was right. We’ve since repurposed our image-processing technology into an algorithm for malaria screening that allows users to quickly and accurately detect and diagnose the presence of malaria parasites — without ever putting eyes to a microscope.
We’re now in the process of developing purpose-built image-processing techniques for reliable detection and diagnosis of many preventable infectious diseases, especially in the developing world.
Imagine — transforming ideas for petrochemical processes to malaria diagnostics. It’s what’s next in helping the developing world fight disease.