Ms Lee and Mr Qi entered the contest in December and created a method that has proved, in early tests, to be as effective as a cardiologist in analysing images of the heart.
"People have been working on this for 15 years — I'm amazed what kind of results came out of this competition in three months," said Andrew Arai, chief of advanced cardiovascular imaging at the National Institutes of Health.
MRIs are used to diagnose heart disease around 1m times a year in the US, Mr Arai said, with cardiologists spending an average of 20 minutes on each image. That could make the algorithm an important addition to a growing field of automated medical imaging, though it faces stringent formal tests before it can be adopted.
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The winning entry used a so-called convolutional neural network, a form of deep learning designed to emulate the way vision works in animals.
Ms Lee said that neither of the pair had worked with neural networks before and had taken software from GitHub, an online repository of open-source software, to solve the challenge. The main problem they faced had been to define the problem in a precise enough way, she added. After that, it was a question of feeding examples of heart MRIs into the neural network and letting it work out the solution.
The availability of such software meant that even complex problems were open to being solved by experts with a more general background in data science, Ms Lee said. "We are both very experienced with working with large amounts of data, and having an intuition about where to look for problems," she said. "It took all my spare time over a period of three months."