17-Year-old Wins $150,000 in Science Talent Search for Remarkable Way to Diagnose Pediatric Heart Disease
In the oldest and most prestigious young adult science competition in the nation, 17-year-old Ellen Xu used a kind of AI to design the first diagnosis test for a rare disease that struck her sister years ago.
With a personal story driving her on, she managed an 85% rate of positive diagnoses with only a smartphone image, winning her $150,000 grand for a third-place finish.
Kawasaki disease has no existing test method, and relies on a physician’s years of training, ability to do research, and a bit of luck.
Symptoms tend to be fever-like and therefore generalized across many different conditions. Eventually if undiagnosed, children can develop long-term heart complications, such as the kind that Ellen’s sister was thankfully spared from due to quick diagnosis.
Xu decided to see if there were a way to design a diagnostic test using deep learning for her Regeneron Science Talent Search medicine and health project. Organized since 1942, every year 1,900 kids contribute adventures.
She designed what is known as a convolutional neural network, which is a form of deep-learning algorithm that mimics how our eyes work, and programmed it to analyze smartphone images for potential Kawasaki disease.
However, like our own eyes, a convolutional neural network needs a massive amount of data to be able to effectively and quickly process images against references.
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For this reason, Xu turned to crowdsourcing images of Kawasaki’s disease and its lookalike conditions from medical databases around the world, hoping to gather enough to give the neural network a high success rate.
Xu has demonstrated an 85% specificity in identifying between Kawasaki and non-Kawasaki symptoms in children with just a smartphone image, a demonstration that saw her test method take third place and a $150,000 reward at the Science Talent Search.
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