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Malaria is among the leading causes of death in many developing countries. But detecting the disease is an uncertain art, prone to human error.

A new, student-led startup at the 91�Թ� aims to take the guesswork out of detecting malaria with smartphone lens attachments and image-recognition software.

Faculty of Medicine writer Carolyn Morris spoke with one of the co-founders, Fatema Chowdhury, to find out more about the endeavor.

Why do we need to improve the way malaria is currently diagnosed? 
In 2013 alone there were 198 million reported cases of malaria and 584,000 deaths, according to the World Health Organization. The key to effective treatment for malaria relies on the efficiency and accuracy malaria parasite identification. The problem is, we don’t have a reliable way to diagnose the disease. The gold standard is microscopy, but this takes having expensive equipment and a trained laboratory technician who can interpret the blood smears. And although it’s considered the best approach at the moment, it’s still not very accurate, with 2009 statistics from Sudan showing an alarming 75.6 per cent false positive rate.

Another way malaria is regularly diagnosed is with rapid diagnostic tests (RDTs), that operate similar to pregnancy tests, which detect malaria in the blood. The problem with these is they are often affected by humidity and have to be thrown out. They also only identify certain strains of malaria, not all four that can infect humans, so other types go undetected. If we can speed up the diagnostic microscopy procedure, and make it accurate and affordable, we can increase the probability of survival for patients.

How will your device work? 
The device would include a smartphone lens attachment with a slide holder, which would work in conjunction with image-detection software. Health professionals could use the attachment to convert their mobile phone’s camera into a microscopic camera, insert blood smear slides and capture an image. They would then run the image through the software, which would detect patterns in the blood smear and identify the presence of malaria. Considering mobile technology is quite prevalent in malaria-endemic areas, this would potentially be a practical, fast and affordable solution.

Our startup involves more than this technology, though. It also includes a collaborative database of blood-smear slide images that people would be adding to and drawing from for research purposes, to improve diagnosis moving forward. We’re planning to begin with malaria, but eventually expand the technology and approach to other diseases as well. This is why we’re calling our project .

Where did you get the idea for this project?
Our team met up in a 91�Թ� Arts and Science Entrepreneurship Program in the summer of 2015 – we’re all 91�Թ� students.Yannie Lai and I both have a life-sciences background, and Tien-Che Lee and Victoria Bukta are in computer sciences. We started chatting about some of the key priorities in global health, and malaria came up as one of the diseases that we need action on for the millennium development goals. When we started to look at different apps and imaging technologies – like the ones used to track skin cancer – we wondered whether the same principle could be used for microscopy diagnosis. As it turns out, this is an area prone to human error, and it’s all about pattern recognition – something that’s in the realm of computer sciences.

Despite the name, a startup isn’t an easy thing to start. How did you turn your idea into a project?
With a ton of help along the way! Through the Arts and Science Entrepreneurship Program, directors Helen Kontozopoulos and Mario Grech taught us essential business concepts and skills to run a successful start-up, and kept us informed about various networking and pitching opportunities. One of these events was the Venture Capital Investment Competition at Rotman last fall, where we received the Start-up of the Year Award. We’ve also received a lot of advice and guidance from 91�Թ� malaria researcher, Professor Ian Crandall. He has been providing us with malaria blood smear slides to start building our software. We’ve also been getting support from the Faculty of Medicine’s Health Innovation Hub (H2i), in terms of networking and capacity-building resources.

What’s next for Collaborative Disease Diagnostics – or CDDx?
We aim to have our minimum viable product completed by this spring, and proceed to refine both the hardware and software based on initial user feedback. We are also in communications with Arrow Electronics, as they are interested in developing the hardware component of our solution. We’re passionate about the possibility of making a difference in global health, and are inspired by the impact our solution could have on the lives of millions of people. In the meantime, we’re learning a lot of new things through this project and it’s been exciting to be able to collaborate with other organizations to make our idea a reality.

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