Diagnosing Malaria with a Mobile Device

A high-school student hopes to change the face of third-world illness with his proposed phone add-ons

Cooked up over a weekend in a room thronged with programmers, a blood-testing kit for smartphones won the first ever YC Hacks competition in Mountain View, CA this month. With a simple, improvised demonstration involving an iPhone and a toilet paper roll, the project entitled Athelas swayed judges and onlookers alike by presenting a cheap, pocket-sized method for detecting malaria and other maladies.

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In brief, Athelas’ contest application proposes using lens attachments with camera phones to magnify and document blood samples and an app to analyze the images. Tanay Tandom, the project’s creator and a high-school senior in Cupertino, CA, explains that, significantly, his product “implement[s] computer vision to algorithmically count and identify cells in the bloodstream to automatically diagnose disease/conditions.” The lens attachment, a 1mm ball lens with an estimated production cost of $5, currently supplies images which allow for an accuracy rate of 70-75% with “live blood samples.” When supplied with training data, the diagnosis program is reported as having an accuracy rate of of 90-95% with cell identification.

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Tandon further explains via collaborative problem solving community ChallengePost that his goal for the project is to provide “a malaria test that requires no expertise, takes a few seconds, and costs next to nothing,” with a large live-saving potential; he hopes that, “through predictive cell counting, Athelas can mimic the process conducted in lab-grade environments in rural areas.” Tandon tells The Next Web:

I strongly believe that artificial intelligence and research can be used to drive innovative changes in society, and my goal as I enter college and the workforce will be to continue working on products such as Athelas that can enact positive changes through the power of computer science.

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However, as the BBC reported, the possibility of the app producing false positives — or incorrect diagnoses — is a major concern among experts. Dr. Mike Chapman of the University of Cambridge’s Department of Haematology cited “no problem with it in principle – but in reality, these need to be carefully calibrated around the right type of diseases.” He also explains that, while other projects have successfully involved the sending of pictures of blood cells to lab technicians, some diseases are much easier to detect than others: “[i]n a laboratory diagnosis, there is a lot of regulation that goes on behind the scenes to make sure that your results are meaningful.”

Dr. Amar Safdar, director of transplant medical diseases at New York University’s Langone Medical Center, echoed this sentiment: “[t]his app will create more confusion then alleviate anxiety,” he explained to the BBC. “The major limitation for this approach is that most viruses require electron microscopic exams to see them.”

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For his prize, Mr. Tandon won an interview with YC’s team of industry leaders to be included in its next group of funded start-ups. The California-based project accelerator has fueled such start-ups such as Airbnb, Reddit, and Dropbox, each having since reached the billion-dollar mark.

While he is currently the sole contributor to his project, he explained by phone that he is in the process of finding laboratory and other facilities to support further research and testing. As he tells The Next Web, “[C]urrently I’m pursuing the work in a more research-oriented fashion, validating the algorithm accuracy as well as testing the microscope attachment on a variety of blood smears and diseased tissue.”

[h/t] The Next Web

Images: Carter Center, Getty Images, Athelas, The Next Web

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