And yet, despite decades of medical advancements, assessing cardiac function remains a time-consuming undertaking. Until, potentially, now.
The results from a global data science competition – the 2016 Data Science Bowl – could fundamentally transform one of the most important, time-intensive heart assessment procedures. It’s the “gold standard” of heart imaging: the cardiac MRI. Increasingly powerful medical imaging technology generates a detailed picture of the heart. Today’s cardiac MRI is a vast improvement from what patients would have experienced as recently as a decade ago. But after the MRI generates a detailed image of the heart, the process reverts to “manual” assessment by a specially trained cardiologist. The doctor must spend 20-30 minutes reviewing the results before proceeding further.
That time spent is more than inconvenience. It reduces the number of patients the cardiologist can see in a day. It reduces the time available to perform other procedures to understand the patient’s heart health and needs. Most importantly, it takes time away that the cardiologist could be spending with his or her patients.
For cardiologists, the ideal solution is an advanced algorithm that could perform an analysis of the cardiac MRI. Such an algorithm could accomplish in a few seconds what currently takes 20-30 minutes to complete. Yet the search for an algorithm that can analyze cardiac medical imaging – to at or near human- level accuracy – has proven elusive.
Enter the Data Science Bowl, a data science competition that puts a complex, global societal challenge before a worldwide audience of participants. Co-sponsored by Booz Allen Hamilton and Kaggle, the event is open to anyone with the skills, curiosity and passion to make a difference. The Data Science Bowl uses the power of the global data science community to solve the previously un-solvable.
