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Using Artificial Intelligence to Catch Irregular Heartbeats

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ECG Readout
Credit: gettyimages/enot-poloskun

Thanks to advances in wearable health technologies, it’s now possible for people to monitor their heart rhythms at home for days, weeks, or even months via wireless electrocardiogram (EKG) patches. In fact, my Apple Watch makes it possible to record a real-time EKG whenever I want. (I’m glad to say I am in normal sinus rhythm.)

For true medical benefit, however, the challenge lies in analyzing the vast amounts of data—often hundreds of hours worth per person—to distinguish reliably between harmless rhythm irregularities and potentially life-threatening problems. Now, NIH-funded researchers have found that artificial intelligence (AI) can help.

A powerful computer “studied” more than 90,000 EKG recordings, from which it “learned” to recognize patterns, form rules, and apply them accurately to future EKG readings. The computer became so “smart” that it could classify 10 different types of irregular heart rhythms, including atrial fibrillation (AFib). In fact, after just seven months of training, the computer-devised algorithm was as good—and in some cases even better than—cardiology experts at making the correct diagnostic call.

EKG tests measure electrical impulses in the heart, which signal the heart muscle to contract and pump blood to the rest of the body. The precise, wave-like features of the electrical impulses allow doctors to determine whether a person’s heart is beating normally.

For example, in people with AFib, the heart’s upper chambers (the atria) contract rapidly and unpredictably, causing the ventricles (the main heart muscle) to contract irregularly rather than in a steady rhythm. This is an important arrhythmia to detect, even if it may only be present occasionally over many days of monitoring. That’s not always easy to do with current methods.

Here’s where the team, led by computer scientists Awni Hannun and Andrew Ng, Stanford University, Palo Alto, CA, saw an AI opportunity. As published in Nature Medicine, the Stanford team started by assembling a large EKG dataset from more than 53,000 people [1]. The data included various forms of arrhythmia and normal heart rhythms from people who had worn the FDA-approved Zio patch for about two weeks.

The Zio patch is a 2-by-5-inch adhesive patch, worn much like a bandage, on the upper left side of the chest. It’s water resistant and can be kept on around the clock while a person sleeps, exercises, or takes a shower. The wireless patch continuously monitors heart rhythms, storing EKG data for later analysis.

The Stanford researchers looked to machine learning to process all the EKG data. In machine learning, computers rely on large datasets of examples in order to learn how to perform a given task. The accuracy improves as the machine “sees” more data.

But the team’s real interest was in utilizing a special class of machine learning called deep neural networks, or deep learning. Deep learning is inspired by how our own brain’s neural networks process information, learning to focus on some details but not others.

In deep learning, computers look for patterns in data. As they begin to “see” complex relationships, some connections in the network are strengthened while others are weakened. The network is typically composed of multiple information-processing layers, which operate on the data and compute increasingly complex and abstract representations.

Those data reach the final output layer, which acts as a classifier, assigning each bit of data to a particular category or, in the case of the EKG readings, a diagnosis. In this way, computers can learn to analyze and sort highly complex data using both more obvious and hidden features.

Ultimately, the computer in the new study could differentiate between EKG readings representing 10 different arrhythmias as well as a normal heart rhythm. It could also tell the difference between irregular heart rhythms and background “noise” caused by interference of one kind or another, such as a jostled or disconnected Zio patch.

For validation, the computer attempted to assign a diagnosis to the EKG readings of 328 additional patients. Independently, several expert cardiologists also read those EKGs and reached a consensus diagnosis for each patient. In almost all cases, the computer’s diagnosis agreed with the consensus of the cardiologists. The computer also made its calls much faster.

Next, the researchers compared the computer’s diagnoses to those of six individual cardiologists who weren’t part of the original consensus committee. And, the results show that the computer actually outperformed these experienced cardiologists!

The findings suggest that artificial intelligence can be used to improve the accuracy and efficiency of EKG readings. In fact, Hannun reports that iRhythm Technologies, maker of the Zio patch, has already incorporated the algorithm into the interpretation now being used to analyze data from real patients.

As impressive as this is, we are surely just at the beginning of AI applications to health and health care. In recognition of the opportunities ahead, NIH has recently launched a working group on AI to explore ways to make the best use of existing data, and harness the potential of artificial intelligence and machine learning to advance biomedical research and the practice of medicine.

Meanwhile, more and more impressive NIH-supported research featuring AI is being published. In my next blog, I’ll highlight a recent paper that uses AI to make a real difference for cervical cancer, particularly in low resource settings.

Reference:

[1] Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP, Ng AY.
Nat Med. 2019 Jan;25(1):65-69.

Links:

Arrhythmia (National Heart, Lung, and Blood Institute/NIH)

Video: Artificial Intelligence: Collecting Data to Maximize Potential (NIH)

Andrew Ng (Palo Alto, CA)

NIH Support: National Heart, Lung, and Blood Institute


Meeting with Congressman Ro Khanna

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Larry Tabak, Congressman Ro Khanna and Francis Collins at the NIH Clinical Center

We had a great visit with Congressman Ro Khanna (center) of California. Our discussion included recent advances in neuroscience, genomics, Big Data, and research on food allergies. NIH Deputy Director Larry Tabak (left) and I welcomed Congressman Khanna to the NIH Clinical Center on July 30, 2018.


Crowdsourcing 600 Years of Human History

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Family Tree

Caption: A 6,000-person family tree, showing individuals spanning seven generations (green) and their marital links (red).
Credit: Columbia University, New York City

You may have worked on constructing your family tree, perhaps listing your ancestry back to your great-grandparents. Or with so many public records now available online, you may have even uncovered enough information to discover some unexpected long-lost relatives. Or maybe you’ve even submitted a DNA sample to one of the commercial sources to see what you could learn about your ancestry. But just how big can a family tree grow using today’s genealogical tools?

A recent paper offers a truly eye-opening answer. With permission to download the publicly available, online profiles of 86 million genealogy hobbyists, most of European descent, the researchers assembled more than 5 million family trees. The largest totaled more than 13 million people! By merging each tree from the crowd-sourced and public data, including the relatively modest 6,000-person seedling shown above, the researchers were able to go back 11 generations on average to the 15th century and the days of Christopher Columbus. Doubly exciting, these large datasets offer a powerful new resource to study human health, having already provided some novel insights into our family structures, genes, and longevity.


Creative Minds: Looking for Common Threads in Rare Diseases

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Valerie Arboleda

Valerie Arboleda
Credit: UCLA/Margaret Sison Photography

Four years ago, Valerie Arboleda accomplished something most young medical geneticists rarely do. She helped discover a rare congenital disease now known as KAT6A syndrome [1]. From the original 10 cases to the more than 100 diagnosed today, KAT6A kids share a single altered gene that causes neuro-developmental delays, most prominently in learning to walk and talk, plus a spectrum of possible abnormalities involving the head, face, heart, and immune system.

Now, Arboleda wants to accomplish something even more groundbreaking. With a 2017 NIH Director’s Early Independence Award, she will develop ways to mine Big Data—the voluminous amounts of DNA sequence and other biological information now stored in public databases—to unearth new clues into the biology of rare disorders like KAT6A syndrome. If successful, Arboleda’s work could bring greater precision to the diagnosis and potentially treatment of Mendelian disorders, as well as provide greater clarity into the specific challenges that might lie ahead for an affected child.


Creative Minds: Building Better Computational Models of Common Disease

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Hilary Finucane

Hilary Finucane

Not so long ago, Hilary Finucane was a talented young mathematician about to complete a master’s degree in theoretical computer science. As much as she enjoyed exploring pure mathematics, Finucane had begun having second thoughts about her career choice. She wanted to use her gift for numbers in a way that would have more real-world impact.

The solution to her dilemma was, literally, standing right by her side. Her husband Yakir Reshef, also a mathematician, was developing a new algorithm at the Broad Institute of MIT and Harvard, Cambridge, MA, to improve detection of unexpected associations in large data sets. So, Finucane helped the Broad team with modeling biomedical topics ranging from the gut microbiome to global health. That work led to her co-authoring a paper in the journal Science [1], providing a strong start to what’s shaping up to be a rewarding career in computational biology.


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