Research shows how machine learning can help doctors discern between two very serious heart conditions with very similar symptoms.

Dr. Partho Sengupta had a hunch. A leading cardiologist now practicing at the West Virginia University Heart and Vascular Institute, Sengupta wanted to know whether the emerging field of machine learning could help solve a  problem that had long vexed heart doctors.

Driven by his conviction and curiosity, Sengupta cold-called data scientists at Saffron, a pioneering artificial intelligence company in North Carolina’s Research Triangle acquired by Intel in 20151, with an idea for a novel experiment. Several phone calls and one proof of concept later, Sengupta and Saffron were able to show that a particular type of machine learning can be a powerful—even lifesaving—aid to cardiologists.

The groundbreaking work also holds promise for delivering on the triple aims of healthcare reform: lowering costs, elevating quality of care, and improving access.

The idea for the experiment had its genesis in Sengupta’s office, where, like every other cardiologist, Sengupta struggled to diagnose between two very different diseases with dangerously similar symptoms.

Constrictive pericarditis is a tricky disease for heart doctors to diagnose. People suffering from the condition have symptoms that look a lot like cardiomyopathy, a constellation of diseases affecting the heart muscle. Patients with either condition have difficulty breathing, are constantly fatigued, suffer from legs and ankles that swell with liquid, and feel weak.

But pericarditis is not heart failure. It’s an inflammation of the double-walled sac that covers the heart. If it’s misdiagnosed as failure of the heart muscle, and treated as such, the condition can be fatal, said Sengupta: “It’s like treating a fever, but not treating the infection. You have to treat the cause.” 

To make the right diagnosis, heart doctors rely on their experience and the wealth of data from echocardiograms. But even with the combined power of training and technology, results are mixed. A skilled and experienced cardiologist like Sengupta can distinguish between the two diseases three out of four times. For other physicians the accuracy rate is closer to one in two.

The big question Sengupta wanted answered was this: Could artificial intelligence discern between these two diseases with greater accuracy than the most skilled doctors, thereby giving physicians a valuable diagnostic tool?

After an internet search led Sengupta to Saffron, it didn’t take long for the company’s data scientists to realize that the cardiologist’s idea for a proof of concept was well worth pursuing.

"It’s like treating a fever, but not treating the infection. You have to treat the cause."

—Partho Sengupta
Chair of Cardiac Innovation,
West Virginia University

"Whenever I use technology on a problem that is bigger than my own interests, the results have been amazing."

—Partho Sengupta
Chair of Cardiac Innovation,
West Virginia University

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