Crowdsource Contest Winner Could Transform How Heart Disease is Diagnosed
Two hedge fund analysts create an algorithm that could revolutionize heart disease diagnosis.
National Institutes of Health, United States
According to the World health Organization (WHO), cardiovascular diseases (CVDs) are the number one cause of death around the world. In the U.S. alone, 1,500 people are diagnosed with heart failure each day.
Although much is known about risk factors, and despite decades of medical advancements, assessing cardiac function is still a time-consuming process. The knock-on effect of this is that it reduces the number of patients a doctor can see (and potentially diagnose) in any given day.
Hoping to tackle the time drains was the Data Science Bowl
, a global crowdsourcing data science competition that invited users to innovate using the power of data.
Can Medical Assessments be Speeded Up?
The specific task that participants were tasked with solving concerned the cardiac MRI, a medical imaging technology that generates a detailed picture of the heart.
After an image has been produced, cardiologists take over with a manual assessment that can take up to 20-30 minutes, time they could be spending with other patients. Contest participants had to come up with an advanced algorithm that could quickly perform an analysis of the cardiac MRI.
“If the cardiology community could have solved this on its own, it would have,” said Dr. Michael Hansen, Ph.D., one of the Principal Investigators from the National Institutes of Health National Heart, Lung, and Blood Institute.
“The Data Science Bowl brings different perspectives, different backgrounds and fresh eyes to the problem. And that is effective.”
During the course of the competition, nearly 200 teams took part. To help them develop their algorithms, contest organizers provided more than 1,000 MRI images from a broad population set, including men and women of different ages.
Nearly 1,400 algorithms were submitted for consideration and the winning one was developed by Qi Liu and Tencia Lee, hedge fund analysts with no previous medical experience. They used a technique known as deep learning, a new area of machine learning research.
In preliminary tests, their algorithm proved to be at least as effective as the human eye, and could do in seconds what it takes cardiologists up to 20 minutes.
“People have been working on this for 15 years — I’m amazed what kind of results came out of this competition in three months,” commented Andrew Arai (chief of advanced cardiovascular imaging at the National Institutes of Health) in an interview with the Financial Times.
The Next Steps
Scientists at the National Institutes of Health will now be working with the winners to test the accuracy of the algorithm in a variety of clinical environments. Ultimately, if successful it could be integrated into existing technology for cardiologists to use.
Crowdsourcing Gets Things Done
Josh Sullivan, senior vice president, Booz Allen Hamilton, believes the competition was a clear demonstration of the power of crowdsourcing to get things done: “This particular challenge was one that the medical community had not been able to solve. It needed to harness the power of crowdsourcing.
“The ability to bring together unique backgrounds, perspectives and expertise is what makes a data science team so effective and it’s what made the Data Science Bowl a success.”
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